Weijing Tao

Weijing Tao, Xiaofeng Yang, Miaomiao Cui et al.

We introduce InteractPro, a comprehensive framework for dynamic motion-aware image composition. At its core is InteractPlan, an intelligent planner that leverages a Large Vision Language Model (LVLM) for scenario analysis and object placement, determining the optimal composition strategy to achieve realistic motion effects. Based on each scenario, InteractPlan selects between our two specialized modules: InteractPhys and InteractMotion. InteractPhys employs an enhanced Material Point Method (MPM)-based simulation to produce physically faithful and controllable object-scene interactions, capturing diverse and abstract events that require true physical modeling. InteractMotion, in contrast, is a training-free method based on pretrained video diffusion. Traditional composition approaches suffer from two major limitations: requiring manual planning for object placement and generating static, motionless outputs. By unifying simulation-based and diffusion-based methods under planner guidance, InteractPro overcomes these challenges, ensuring richly motion-aware compositions. Extensive quantitative and qualitative evaluations demonstrate InteractPro's effectiveness in producing controllable, and coherent compositions across varied scenarios.

Weijing Tao, Biwen Lei, Kunhao Liu et al.

Text-to-Avatar generation has recently made significant strides due to advancements in diffusion models. However, most existing work remains constrained by limited diversity, producing avatars with subtle differences in appearance for a given text prompt. We design DivAvatar, a novel framework that generates diverse avatars, empowering 3D creatives with a multitude of distinct and richly varied 3D avatars from a single text prompt. Different from most existing work that exploits scene-specific 3D representations such as NeRF, DivAvatar finetunes a 3D generative model (i.e., EVA3D), allowing diverse avatar generation from simply noise sampling in inference time. DivAvatar has two key designs that help achieve generation diversity and visual quality. The first is a noise sampling technique during training phase which is critical in generating diverse appearances. The second is a semantic-aware zoom mechanism and a novel depth loss, the former producing appearances of high textual fidelity by separate fine-tuning of specific body parts and the latter improving geometry quality greatly by smoothing the generated mesh in the features space. Extensive experiments show that DivAvatar is highly versatile in generating avatars of diverse appearances.