Daily Papers

This paper presents an approach to decomposing animated graphics into sprites, a set of basic elements or layers. Our approach builds on the optimization of sprite parameters to fit the raster video. For efficiency, we assume static textures for sprites to reduce the search space while preventing artifacts using a texture prior model. To further speed up the optimization, we introduce the initialization of the sprite parameters utilizing a pre-trained video object segmentation model and user input of single frame annotations. For our study, we construct the Crello Animation dataset from an online design service and define quantitative metrics to measure the quality of the extracted sprites. Experiments show that our method significantly outperforms baselines for similar decomposition tasks in terms of the quality/efficiency tradeoff.

In this paper, we introduce GaussianMotion, a novel human rendering model that generates fully animatable scenes aligned with textual descriptions using Gaussian Splatting. Although existing methods achieve reasonable text-to-3D generation of human bodies using various 3D representations, they often face limitations in fidelity and efficiency, or primarily focus on static models with limited pose control. In contrast, our method generates fully animatable 3D avatars by combining deformable 3D Gaussian Splatting with text-to-3D score distillation, achieving high fidelity and efficient rendering for arbitrary poses. By densely generating diverse random poses during optimization, our deformable 3D human model learns to capture a wide range of natural motions distilled from a pose-conditioned diffusion model in an end-to-end manner. Furthermore, we propose Adaptive Score Distillation that effectively balances realistic detail and smoothness to achieve optimal 3D results. Experimental results demonstrate that our approach outperforms existing baselines by producing high-quality textures in both static and animated results, and by generating diverse 3D human models from various textual inputs.

Significant advancements have been made in developing parametric models for digital humans, with various approaches concentrating on parts such as the human body, hand, or face. Nevertheless, connectors such as the neck have been overlooked in these models, with rich anatomical priors often unutilized. In this paper, we introduce HACK (Head-And-neCK), a novel parametric model for constructing the head and cervical region of digital humans. Our model seeks to disentangle the full spectrum of neck and larynx motions, facial expressions, and appearance variations, providing personalized and anatomically consistent controls, particularly for the neck regions. To build our HACK model, we acquire a comprehensive multi-modal dataset of the head and neck under various facial expressions. We employ a 3D ultrasound imaging scheme to extract the inner biomechanical structures, namely the precise 3D rotation information of the seven vertebrae of the cervical spine. We then adopt a multi-view photometric approach to capture the geometry and physically-based textures of diverse subjects, who exhibit a diverse range of static expressions as well as sequential head-and-neck movements. Using the multi-modal dataset, we train the parametric HACK model by separating the 3D head and neck depiction into various shape, pose, expression, and larynx blendshapes from the neutral expression and the rest skeletal pose. We adopt an anatomically-consistent skeletal design for the cervical region, and the expression is linked to facial action units for artist-friendly controls. HACK addresses the head and neck as a unified entity, offering more accurate and expressive controls, with a new level of realism, particularly for the neck regions. This approach has significant benefits for numerous applications and enables inter-correlation analysis between head and neck for fine-grained motion synthesis and transfer.

Object manipulation in images aims to not only edit the object's presentation but also gift objects with motion. Previous methods encountered challenges in concurrently handling static editing and dynamic generation, while also struggling to achieve fidelity in object appearance and scene lighting. In this work, we introduce OMG3D, a novel framework that integrates the precise geometric control with the generative power of diffusion models, thus achieving significant enhancements in visual performance. Our framework first converts 2D objects into 3D, enabling user-directed modifications and lifelike motions at the geometric level. To address texture realism, we propose CustomRefiner, a texture refinement module that pre-train a customized diffusion model, aligning the details and style of coarse renderings of 3D rough model with the original image, further refine the texture. Additionally, we introduce IllumiCombiner, a lighting processing module that estimates and corrects background lighting to match human visual perception, resulting in more realistic shadow effects. Extensive experiments demonstrate the outstanding visual performance of our approach in both static and dynamic scenarios. Remarkably, all these steps can be done using one NVIDIA 3090. Project page is at https://whalesong-zrs.github.io/OMG3D-projectpage/

Image-based virtual try-on systems,which fit new garments onto human portraits,are gaining research attention.An ideal pipeline should preserve the static features of clothes(like textures and logos)while also generating dynamic elements(e.g.shadows,folds)that adapt to the model's pose and environment.Previous works fail specifically in generating dynamic features,as they preserve the warped in-shop clothes trivially with predicted an alpha mask by composition.To break the dilemma of over-preserving and textures losses,we propose a novel diffusion-based Product-level virtual try-on pipeline,\ie PLTON, which can preserve the fine details of logos and embroideries while producing realistic clothes shading and wrinkles.The main insights are in three folds:1)Adaptive Dynamic Rendering:We take a pre-trained diffusion model as a generative prior and tame it with image features,training a dynamic extractor from scratch to generate dynamic tokens that preserve high-fidelity semantic information. Due to the strong generative power of the diffusion prior,we can generate realistic clothes shadows and wrinkles.2)Static Characteristics Transformation: High-frequency Map(HF-Map)is our fundamental insight for static representation.PLTON first warps in-shop clothes to the target model pose by a traditional warping network,and uses a high-pass filter to extract an HF-Map for preserving static cloth features.The HF-Map is used to generate modulation maps through our static extractor,which are injected into a fixed U-net to synthesize the final result.To enhance retention,a Two-stage Blended Denoising method is proposed to guide the diffusion process for correct spatial layout and color.PLTON is finetuned only with our collected small-size try-on dataset.Extensive quantitative and qualitative experiments on 1024 768 datasets demonstrate the superiority of our framework in mimicking real clothes dynamics.