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Генеративные 3D модели

Введение

Салют, Хабр! На связи Игорь Пасечник — технический лид направления XR RnD SberDevices. Сегодня я хочу рассказать про одно из наших направлений исследований — разработку генеративных моделей для 3D-контента.

Современные методы генерации 2D-контента, такие, как 2D-диффузионные модели (Kandinsky 3.0, SDXL), уже достигли впечатляющих результатов и несколько лет являются неотъемлемой частью современности, генеративные видео модели также активно развиваются. Кульминацией развития таких подходов, вероятно, станет представленная не так давно модель Sora. Тем не менее большинство из этих моделей до сих пор испытывают проблемы при генерации консистентных 3D-сцен и объектов.

С другой стороны, существует конвенциональная 3D-графика, а также огромная индустрия и множество прикладных областей, включая игры, XR, дизайн, архитектуру, маркетинг, 3D-проектирование, где используются пайплайны на основе 3D-графики и производится контент на их основе. Методы создания 3D-моделей, такие, как ручное моделирование, 3D-сканирование и фотограмметрия, могут быть трудоёмкими, дорогостоящими и требующими специальных навыков. 3D-продакшн в общем виде использует множество инструментов для создания и рендеринга тяжелой фотореалистичной графики, адаптация генеративных 3D-пайплайнов под такие подходы достаточно тяжела из-за множества инструментов, которые такие пайплайны должны поддерживать. Также адаптация больших латентных генеративных 2D-моделей вроде SORA для прикладных задач фотореалистичной графики может стать альтернативой классическими пайплайнам на основе физического моделирования. Тем не менее, на текущий момент пайплайны работы с графикой, использующие базовый набор примитивов, включая меши, PBR-текстуры, простые модели освещения, закрывают множество прикладных задач и также могут быть востребованы у массового пользователя в случае их демократизации.

Учитывая это, мы решили свести свои исследования в области генеративного 3D к решению нескольких задач:

Научиться генерировать консистентные multi-view изображения объектов по тексту или картинкам, используя 2D-генеративные модели.
Научиться генерировать 3D-ассеты для практического использования по тексту или картинкам.
Научиться делать быструю и достаточно качественную 3D-реконструкцию.
Научиться создавать консистентные визуализации объектов (3D-гифки).

Мы разработали семейство генеративных 3D-моделей на основе 2D-моделей Сбера, проведя работу с данными, выбрав лучшие компоненты из доступных на текущий момент архитектур, а также внеся дополнительные новшества, достигнув SOTA-результатов относительно open-source и проприетарных моделей.

Общая архитектура пайплайна (Text-to-3D)

На текущий момент существует несколько методов быстрой генерации 3D-контента по тексту:

С помощью обучения single-view реконструктора можно получать 3D-объект из любой модели генерации картинок (Kandinsky, Stable Diffusion и прочие). Данный реконструктор может быть feed-forward-моделью как в LRM без вариативности в генерации новых ракурсов, либо через диффузию, как DMV3D с возможностью обусловливания на текст напрямую, либо с обусловливанием на изображение.
По аналогии можно обучить реконструктор sparse-view и использовать диффузионную модель генерации картинок, дообученную на генерацию sparse-view, для получения 3D-объектов. Свой sparse-view генератор изображений представили в ImageDream, MVDream, Instant3D, SPAD, CRM, Wonder3D. В реконструкторе sparse-view модели были обучены с разными рендерингами: volumetric rendering используется в трансформерной модели Instant3D, PF-LRM, гауссовский рендеринг используется в GRM, LGM, mesh-based рендеринг flexicubes используется в CRM.
В качестве генераторов dense-view используют диффузионные модели для видео, обученные генерировать облеты объектов в работах V3D, VFusion3D, SV3D. Для данных dense-view генераций уже необязательно использовать какие-то дополнительные реконструкционные модели.
Также долгое время в качестве текст-3D генераторов использовались модели на основе дистилляции текстового промпта через SDS loss. Данные методы работали по-объектно и занимали большое время на генерацию. Недавно было представлено несколько моделей на основе дистилляции ATT3D, AToM и LATTE3D, обученные на множестве промптов одновременно без использования 3D-данных в качестве референса.
Также есть подходы, которые учат текст-3D модели напрямую, без использования картиночных или видео генераторов. Один из таких подходов GVGEN учится на подсете из 36 тыс. объектов обжаверса, использует гауссовый рендеринг, для которого как раз и нужно иметь 3D-объект, а не его рендеринг.

Изучив все возможные варианты, мы пришли к выводу, что для нашей задачи оптимальным является использование двухстадийного подхода:

Модель первого этапа: дообученная 2D-диффузионная модель (SDXL/Kandinsky 3.0) для генерации консистентных multi-view картинок. Это позволяет сохранить широкий спектр генерируемых образов и визуальное качество изображений, унаследованных от базовой модели.
Модель второго этапа: обучение универсальных моделей реконструкторов для генерации конечного 3D-представления, которые могут работать как на сгенерированных картинках из первого этапа, так и для реконструкции объектов по реальным фотографиям.

В рамках такого подхода можно выделить четыре относительно независимых компонента:

модель первого этапа;
реконструирующий генератор;
латентное 3D-представление;
алгоритм дифференцируемого рендерера.

При создании 3D-объектов мы хотели дать модели всю необходимую информацию о сцене, исключив неопределенности в аспекте геометрии и текстуры. Для получения достаточной информации о генерируемом объекте требуется иметь несколько фотографий сцены с нескольких точек обзора с оптимальными параметрами расположения камер для перекрытия областей обзора. Мы пришли к выводу, что в контексте наших задач не имеет смысла использовать более четырёх ортогональных точек зрения на объект, поэтому мы дообучаем 2D-диффузионную модель для генерации четырех ортогональных ракурсов объекта. Это количество точек обзора является достаточным для реконструкции сцены и сохранения детализации текстур, текущее разрешение синтеза лучших моделей (1024 × 1024) позволяет разделить его на сетку 2 × 2, обеспечивая всё ещё приемлемое качество каждого отдельного вида объекта (512 × 512). Вторым этапом модели является реконструктор, который, используя четыре поступивших на вход изображения, создаёт промежуточное представление сцены в виде триплейнов — ортогональных латентных плоскостей. Наиболее ресурсоёмкой операцией при таком подходе является диффузионная модель. Применив дистилляцию к зафайнтюненой модели первого этапа, мы можем сократить общее время созданий 3D-ассета до 4 секунд.

Рассматривая данный подход, финальное качество работы модели будет определяться следующими факторам:

Качеством модели первого этапа, уровнем её детализации;
Качеством работы реконструктора;
Разрешающей способностью 3D-представления;
Разрешающей способностью и экспрессивностью рендерера.

Для скалирования подобных моделей необходимо пропорционально улучшать все 4 фактора.

Рассмотрим поподробнее все этапы обучения, а также то, что нам показалось важным.

Первый этап. Text to consistent multi-view

Данные

Так как размеры 3D-датасетов (GSO, ABO, Shapenet, Objaverse, OmniObject3D) не так велики, как используемые для обучения 2D-диффузионных моделей (LAION), то надо попытаться выжать из них всё. В первую очередь для подготовки данных к этому этапу обучения необходимо обработать с помощью пайплайна рендеринга используемые объекты с четырёх ортогональных направлений. Далее для каждой получившейся картинки получить текстовое описание через модель-разметчик. Оптимальным вариантом нам показалось использовать LLaVA-1.5 и синтезировать информацию из нескольких описаний в единое лаконичное описание сцены. Для более удобного отбора данных мы рассчитали значения CLIP и оценки эстетичности от предсказателя эстетики (обученного на классификаторе LAION-aesthetics). Также мы составили небольшую выборку 3D-моделей, разделили их на две категории «красивые» и «некрасивые», объединили свойства CLIP и обучили небольшой SVM-классификатор. Учитывая, что мы используем на данном этапе синтетические данные, ключевой особенностью, которая редко рассматривается в академической литературе, являются параметры рендеринга освещения материалов. Регулируя эти параметры, мы можем влиять на качество и особенности финальной модели, а также эстетического восприятия пользователем генерируемых объектов.

Обучение

Процесс дообучения модели аналогичен обучению основной модели: на вход модели подаются пары «текст-изображение», представленные в виде сетки из четырёх рендеров объекта в определённом порядке. Эти данные проходят через текстовый и визуальный энкодеры, результатом чего становятся текстовый эмбеддинг и эмбеддинг изображения. Затем представление изображения зашумляется в соответствии с конфигурацией расписания диффузии и передаётся на вход стандартной модели типа U-Net с условным входом в виде текстового эмбеддинга. На выходе получается прогноз модели об уровне зашумления латентного представления.

После завершения этапа обучения в течение 5 часов на 16 GPU мы получаем модель, которая способна на основе текстового описания синтезировать один и тот же объект или сцену с нескольких ракурсов, предоставляя полную информацию о пространственном представлении будущей 3D-модели.

Процесс дообучения модели. Текстовый запрос «мопед»

Благодаря тому, что мы переиспользовали диффузионную модель и дообучали её на небольшом датасете, она не растеряла способности генерировать объекты не из обучающей выборки, однако выучила новую способность — теперь она умеет видеть их с новой стороны. В процессе обучения происходит несколько процессов — смещение модели в сторону стиля, заданного синтетическим данными, деградация исходного разнообразия модели, снижение степени соответствия генерируемых изображений промту, а также увеличение степени консистентности четырех проекций объектов между собой. При длительной оптимизации модель сходится к максимальным 3D консистеным объектам, но при этом падает вариативность геометрии и текстур, поэтому локальный минимум с наилучшим балансом всех факторов достаточно узок и сильно зависит от гиперпараметров, а также оптимального набора данных. Для валидации и выбора оптимальной итерации можно использовать ошибку реконструкции модели второго этапа а также CLIP score.

Пример генерации по текстовому запросу «сердце человека»

Пример генерации по текстовому запросу «яркий цвет гриб растущий на бревне»

Механизмы контроля генерации

Несмотря на успешность дообучения модели, часто требуется дополнительное управление моделью, чтобы гарантировать, что на итоговом изображении четыре синтезированных рендера будут разделены и каждый будет соответствовать проекции обособленного объекта, а не сцены с явно выраженным диффузионным фоном. Для этого приходится прибегать к различным хитростям. Например, можно использовать изображение, состоящее из 4 равномерно распределенных 2D-гауссиан для формирования стартовой точки при генерации итоговых изображений. После создания такого исходного изображения необходимо получить его латентное представление и добавить шум в соответствии с расписанием диффузионного процесса. Этим же методом можно контролировать размер объекта.

Влияние инициализации визуального латента. Текстовый запрос «Астронавт на лошади»

Второй этап. Реконструтор

Общий процесс обучения

В реконструируемом этапе общая идея модели, которую мы используем, выглядит следующим образом:

На вход мы принимаем 4 картинки объекта с разных ракурсов и с помощью реконструируемого генератора получаем из этих картинок triplane-представление, 3 feature-плоскости, на которые проецируется объект.
Затем мы семплируем точки в 3D-пространстве в зависимости от используемой модели рендеринга, получаем соответствующие этим точкам features из триплейнов и декодируем из них с помощью MLP в sdf/opacity/tex_features, которые потом будут использоваться в рендеринге.
Рендерим объект из полученного триплейна с четырёх случайных ракурсов, взятых из датасета, оптимизируем с лоссом LPIPS + mse между отрендеренными изображениями и изображениями из датасета, соответствующим этим ракурсам.

В рамках такой схемы мы рассмотрели два вида архитектур генераторов и несколько архитектур рендерера.

Генератор триплейна на основе трансформера (instant3d)

Одной из первых работ, использующих такой подход к реконструкции, является Instant3d, она же на текущий момент демонстрирует одни из лучших результатов по синтетическим метрикам.

Однако базовая архитектура имеет ряд недостатков, не указанных в статье, а именно — ограниченную детализацию текстуры, блочные артефакты для высокочастотных регионов, низкое разрешение выходного триплейна. Модифицировав исходную архитектуру, включая расширение внутреннего представления генератора до 64 × 64, использовав DINOv2 энкодеры с регистрами, а также более сложный upsampler, мы смогли получить лучшие результаты и избавиться от артефактов, однако общая архитектура модели вносит ограничения в масштабируемость такого подхода, потому что увеличение количества параметров генератора не влияет в явном виде на детализацию текстур на объекте, а лишь на способность генератора фьюзить входные данные в целевое представление объекта.

Генератор на основе U-Net

Альтернативным вариантом для реконструкционного генератора является использование генератора на основе U-Net для предсказания триплейнов. Один из таких вариантов в частности используется в ряде подходов, включая CRM. Наилучшие результаты в рамках SBS в задаче text-to-3D из четырёх ортогональных картинок, полученных в результате применения первого этапа, получил именно такой подход. Четыре картинки каноническим образом могут быть отождествлены с рассматриваемой триплейновой репрезентацией. На наш взгляд предсказание более чем четырёх view на первом этапе является избыточным, поэтому для горизонтального триплейна используется обучаемый позиционный эмбеддинг. Тем не менее, на картинке видно, что модели удается выучить хорошую репрезентацию для этого триплейна. Основное преимущество данного генератора в возможности генерировать триплейны в разрешении равном входным картинкам (512x512), что позволяет повысить качество деталей при рендеринге, в сравнении с instant3d-генератором, максимальное разрешение триплейнов для которого не превышает 128 без использования апсемлера. Число каналов в используемом U-Net для каждого разрешения: [128, 256, 256, 512, 512, 1024], начиная с 512 и заканчивая 16, по 2 ResNet блока для каждого разрешения, что в результате дает около 300 млн обучаемых параметров. Также, начиная с разрешения 64, используется self-attention.

Рендеринг

На текущий момент существует множество подходов к дифференцируемому нейронному рендерингу: стандартный волюметрик, NeuS, gaussian splatting, DMTet, FlexiCubes и множество других. Проведя анализ и эксперименты, мы выбрали два рендерера для нашего пайплайна и двухэтапную схему обучения. На первом этапе мы обучаемся, используя волюметрик рендерер со стандартной экспоненциальной функцией активации — он хорошо изучен, стабильно оптимизируется и достигает высоких значений PSNR. На втором этапе мы дообучаем реконструктор в большем разрешении с рендером на основе DMTet и растеризацией, адаптируя модель к surface-based-геометрии и целевому бюджету по количеству вершин в меше, что позволяет нам впоследствии получать лучшую геометрию при экспорте 3D-ассетов. Перспективным также является использование gaussian splatting, который показывает хорошие результаты и метрики на задачах dense реконструкции, однако исходя из прошлых экспериментов мы пришли к выводу, что наивная адаптация такого подхода к генеративному сетапу требует отказа от нелинейных оптимизаций вроде клонирования и сплита гауссиан, что нивелирует преимущества метода. Также качество, демонстрируемое на задаче реконструкции, вероятно, не может быть обеспечено с текущими архитектурам и бюджету по параметрам генератора.

Для волюметрик-рендерера мы используем достаточно стандартные параметры по аналогии с Instant3d и других подходов, а вот относительно DMTet стоит сказать пару слов более подробно.

DMTet рендеринг

Альтернативным вариантом дифференцируемого рендеринга является DMTet. Алгоритм DMTet состоит из деформируемой тетраэдральной сетки, содержащей функцию расстояния до объекта, signed distance function (sdf), которая в нашем случае аппроксимируется из триплейна на выходе генератора; и дифференцируемого алгоритма marching tetrahedra, который преобразует неявное представление sdf в явное представление поверхностной сетки. У DMTet существуют следующие преимущества перед volumetric рендерингом:

В процессе работы алгоритма получается явное представление 3D-модели в виде меша (набор вершин и граней). Такое представление можно без постпроцессинга использовать в 3D-сценах.
DMTet позволяет разделить явным образом представление геометрии и текстуры в разные компоненты. Такое разделение позволяет замораживать геометрическую компоненту для тюнинга текстурной.
Отделение текстурной компоненты также открывает возможность разделения текстуры на параметры, такие как base color/albedo, specular, metallic (пример nvdiffrec). То есть, DMTet может выдавать текстуру для PhysicallyBased Rendering (PBR).

Переход от volumetric к DMTet

DMTet часто используется для получения мешей и текстур высокого качества в задачах реконструкции единичных объектов, например, nvdiffrec, Fantasia3d или отдельных категорий больших датасетов, например, GET3D. Особенность нашего эксперимента заключается в вариативности данных. Простая замена volumetric рендера на DMTet в нашей реконструкционной модели приводит к ухудшению сходимости модели, вплоть до обучения нулевых функций расстояния sdf. В нашем реконструкторе DMTet выступает финальным этапом тюнинга генератора, позволяющим улучшить геометрию, получить разложение текстуры на компоненты для PBR.

Технические особенности обучения DMTet

DMTet-алгоритм в нашем реконструкторе используется для улучшения качества меша и текстур в большем разрешении, поэтому мы используем достаточно большую тетраэдральную сетку 256 × 256. Триплейн на выходе генератора разделяется на два, а именно геометрический и текстурный. Их размеры тоже увеличиваются по сравнению с volumetric-реконструкцией до 256 для геометрического и 512 для текстурного. На текущем этапе мы не используем алгоритм полного разделения текстуры на параметры PBR, как в, например, Fantasia3D. Корректный PBR-рендеринг требует от модели предсказания не только текстурных свойств объекта, но и генерации окружения модели. Такой подход увеличивает размер модели и замедляет сходимость. В качестве альтернативы полного PBR-разложения мы используем предобученную модель света ENVIDR. Выучив, однако, геометрическую часть DMTet, текстурную можно заменить на корректное PBR-разложение при пообъектном тюнинге, где окружение модели статично и не требует дополнительного генератора.

Обучение реконструирующих моделей

Данные

Для обучения мы используем объединение датасетов Objaverse 1.0 и OmniObject3D, а также Objaverse XL и MVimgNet. Каждый объект из OmniObject3D рендерился со 100 случайных камер на сфере радиуса 4, в качестве обучения использовались все объекты, без дополнительной фильтрации с нашей стороны. В датасете Objaverse, Objaverse XL много объектов низкого качества: не текстурированные, случайные расстановки геометрических объектов, плоскости, что потребовало дополнительной фильтрации датасета. Для этого мы посчитали aesthetic score четырёх противоположных рендерингов для каждого объекта и отфильтровали те объекты, что в среднем дают оценку меньше 3,5. Наилучшие результаты показало обучение в 2 этапа. На первом этапе модель обучается на большой базе с низким порогом отсечения по качеству, на втором этапе — используя только 30% самых лучших данных.

Ресурсы и скорость

Мы провели стандартную инженерную оптимизацию, используемую при обучении больших моделей и неоднократно описанную в рекомендация pytorch и гайдах от крупных компаний. Однако в нашем случае был найден отдельный момент, заслуживающий внимания. Одним из самых узких мест в моделях, использующих сэмплирование из триплейна, является backward стандартной функции grid_sample. Мы написали модифицированную версию torch.nn.functional.grid_sample, которая считает градиенты только по триплейну, а также добавили несколько оптимизаций для ускорения интерполяции градиента в соседних элементах триплейна. Это позволило ускорить backward в 2 раза.

В сумме все оптимизации позволили получить качество, соответствующее базовой модели instant3D, используя 32 карты A100 за 5 дней против 128 карт на 7 дней, как в базовом эксперименте.

Результаты реконструкции на данных из GSO

Оценка реконструируемой модели

Изучив текущие методы валидации подобных моделей, мы пришли к выводу, что наиболее распространенным вариантом является датасет GSO (Google Scanned Objects), состоящий из сканированных реальных объектов, однако этот датасет не имеет референсной методики измерения, и в разных работах используются разные опорные кадры и метод рендеринга референсных картинок, что не позволяет достоверно сравнивать подходы. Мы использовали параметры валидации из Instant3D — входные изображения брались с фиксированных позиций на elevation 20 и азимутом 0, 90, 180, 270. Метрики измерялись на разрешении 128.

	PSNR↑	LPIPS↓	SSIM↑
Instant3d paper reference	26,54	0,0643	0,8934
Ours transformer free view + volumetric	24,16	0,033	0,88
Ours transformer free view + DMTet	21,38	0,062	0,82
Ours U-Net orthogonal 4-view + volumetric	23,60	0,036	0,87
Ours U-Net orthogonal 4-view + DMTet	23.58	0.075	0.88

Метрики в разрешении 512.

	PSNR↑	LPIPS↓	SSIM↑
Ours transformer free view + volumetric	24,24	0,069	0,876
Ours transformer free view + DMTet	20,84	0,073	0,84
Ours U-Net orthogonal 4-view + volumetric	23,63	0,057	0,87
Ours U-Net orthogonal 4-view + DMTet	22.8	0.096	0.87

Оценка и SBS

При выборе стратегии валидации для модели мы применяли как синтетические метрики, так и результаты side-by-side тестирования. Синтетические тесты и сравнение метрик позволяют оценить этапы, где можно достичь некоторого численного идеала, например, при реконструкции. Оценка визуальной привлекательности и консистентности генеративной модели с помощью набора чисел более сложна, и для этого мы проводили субъективные исследования с участием групп разметки.

Мы использовали несколько банков текстовых описаний: в качестве базового использовали DF411 из статьи DreamFusion, который включает 415 сложных композиционных промптов (например: «Кенгуру сидит на лавочке и играет на аккордеоне»), часто используемых для валидации методов преобразования текста в 3D-модели. Однако она ограничена и не включает множество практических кейсов, в связи с этим мы составили собственный набор текстовых промптов, собранных из различных публичных источников, включая ручной сбор неудачных примеров, отобранные по разным критериями промты 2D-бенчмарка JourneyDB, тестовую базу gpt3eval.

Для оценки моделей и различных изменений мы предоставляли разметчикам два варианта видео виртуального облета объекта, сгенерированных двумя моделями. Затем они должны были ответить на несколько простых вопросов: «Какой объект вам больше нравится?» и «Какое видео лучше соответствует текстовому описанию?». Для более детальных UX-исследований им требовалось объяснить свое решение, а также указать, что им нравится или не нравится в отдельных видео.

Такой подход способен оценить целиком end-to-end пайплайн, включая качество реконструтора, соответствие промпту, консистентность, уровень детализации, эстетическую и художественную привлекательность.

В качестве бейзлайна мы выбрали одну из наиболее современных проприетарных моделей от Luma AI (Genie).

DF411	Ours	Genie
Более красивая	38	62
Соответствие промту	28	72

Примеры генераций

Использование предобученных 2D-диффузионных моделей позволяет использовать все стилевые модификаторы, хорошо изученные для 2D-кейсов, мы сохраняем все разнообразие исходной 2D-модели.

Генерации текущего поколения моделей дают достаточно интересные результаты, хотя в среднем худшее соответствие промпту, чем 2D-модель, и меньший success rate, учитывая необходимость генерации консистентных объектов.

Заключение

Адаптация 2D-диффузионных картиночных и видео моделей к консистентной генерации, используя ограниченные вычислительные ресурсы и синтетические данные, является промежуточных шагом между моделями текущего поколения и моделями следующего поколения вроде Sora, обученных на огромном вычислительном ресурсе и большом количестве данных. Преимущество подобных моделей в том, что они работают быстро и позволяют использовать текущее поколение конвенциональных технологий создания и доставки 3D-контента на клиентские устройства.

В текущий момент мы прорабатываем дополнительный функционал, включая генерацию PBR-текстур, hi-res-ассетов, оптимальной топологии меша, а также повышения качества и детализации моделей.

В ближайшее время мы собираемся предоставить доступ к подробному техническому репорту, метрикам, валидационным корзинам. А сейчас наши модели доступны в тестовом доступе на developers.sber.ru. Следите за новостями!

Коллектив авторов: Михаил Мазуров, Наталья Соболева, Дарья Мусаткина, Фанзиль Набияров, Данил Ельцов, Марина Ярославцева, Игорь Пасечник, а также наша продуктовая команда Яна Чаруйская и Кирилл Субботин.
Также рассказ от Михаила Мазурова с конференции GIGA R&D DAY.

По всем вопросам и предложениям, а также в поисках сотрудничества просьба связываться в телеграмме @ipasechnik @charuyskaya

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19 апр. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

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17.07.24 02:26 thompsonrickey

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06.09.24 01:35 Celinagarcia

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23.09.24 18:56 matthewshimself

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01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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21.11.24 08:02 Emily Hunter

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22.11.24 23:43 teresaborja

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24.11.24 02:21 [email protected]

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25.11.24 02:19 briankennedy

COMMENT ON I NEED A HACKER TO RECOVER MONEY FROM BINARY TRADING. HIRE FASTFUND RECOVERY
25.11.24 02:20 briankennedy

After countless hours of research and desperate attempts to find a solution, I stumbled upon FASTFUND RECOVERY. It was like finding an oasis in the middle of a desert. Their website promised to help victims of scams reclaim what was rightfully theirs, and I instantly knew I had to give them a shot. Before diving headfirst into the recovery process, I wanted to make sure that FASTFUND RECOVERY was the real deal. So, I did my due diligence and looked into their expertise and reputation. To my relief, I found that they had an impeccable track record, successfully assisting countless individuals in recovering their lost funds. Their team consisted of experts in cybersecurity and financial fraud, armed with the knowledge and tools needed to tackle even the most intricate scams. With their reputation preceding them, I felt a renewed sense of hope. FASTFUND RECOVERY successfully came to my aid and got back the amount I lost to these scammers and for this, I am sending this article for clarification. The info of FASTFUND RECOVERY is email: Fastfundrecovery8 (@)Gmail (.) com. Web fastfundrecovery(.)com. (W/A 1 807/500/7554)
26.11.24 21:59 [email protected]

In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

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26.11.24 23:12 rickrobinson8

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27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.
01.12.24 17:21 KollanderMurdasanu

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02.12.24 23:02 ytre89

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04.12.24 22:24 andreygagloev

When I first heard about Bitcoin back in 2018, I was skeptical. The idea of a decentralized, digital currency seemed too good to be true. But I was intrigued as I learned more about the technology behind it and its potential. I started small, investing just a few hundred dollars, dipping my toes into the cryptocurrency waters. At first, it was exhilarating to watch the value of my investment grow exponentially. I felt like I was part of the future, an early adopter of this revolutionary new asset. But that euphoria was short-lived. One day, I logged into my digital wallet only to find it empty - my Bitcoin had vanished without a trace. It turned out that the online exchange I had trusted had been hacked, and my funds were stolen. I was devastated, both financially and emotionally. All the potential I had seen in Bitcoin was tainted by the harsh reality that with decentralization came a lack of regulation and oversight. My hard-earned money was gone, lost to the ether of the digital world. This experience taught me a painful lesson about the price of trust in the uncharted territory of cryptocurrency. While the technology holds incredible promise, the risks can be catastrophic if you don't approach it with extreme caution. My Bitcoin investment gamble had failed, and I was left to pick up the pieces, wiser but poorer for having placed my faith in the wrong hands. My sincere appreciation goes to MUYERN TRUST HACKER. You are my hero in recovering my lost funds. Send a direct m a i l ( muyerntrusted ( @ ) mail-me ( . )c o m ) or message on whats app : + 1 ( 4-4-0 ) ( 3 -3 -5 ) ( 0-2-0-5 )
12.12.24 00:35 amandagregory

HOW TO HIRE A HACKER TO RECOVER STOLEN BITCOIN WITH FASTFUND RECOVERY
12.12.24 00:35 amandagregory

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19.12.24 17:07 rebeccabenjamin

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