Hugging Face Trending Papers
Sep 8

ReMoMask-2: Latent Retrieval-Augmented Masked Motion Generation

ReMoMask-2 is a retrieval‑augmented text‑to‑motion generation framework that improves on complex motion descriptions by addressing coarse retrieval and representation gaps. It introduces a structure‑aware RAG pipeline with Hierarchical Bidirectional Momentum contrastive learning, Semantic Spatial‑Temporal Attention, and Topology Structured Masking, and rebuilds the retrieval database in the generator’s latent space using a lightweight projector. Experiments on HumanML3D, KIT‑ML, and SnapMoGen show state‑of‑the‑art retrieval accuracy and the lowest FID scores, with a single mask‑transformer stage delivering faster inference than the previous two‑stage design.

arXiv Computer Vision
Sep 21

CompAdapt: Adaptable Composite Motion Modeling for Physics-Consistent Text-to-Video Generation

CompAdapt is a physics-consistent text-to-video generation framework that extends diffusion-based models to handle composite physical behaviors such as coupled motions, multi-stage transitions, and multi-object collisions. It translates natural language prompts into structured physical semantics, enabling end-to-end specification of motion types, temporal relations, and initial parameters. The system introduces dynamics-aware prior matching for one-shot adaptation to new physical environments and a physics-aware latent feature fusion module to enhance visual fidelity during fast, complex motion, outperforming existing physics-constrained baselines on physics-focused T2V benchmarks.

By Haoran Qin (Harbin Institute of Technology, China), Renlong Wu (Harbin Institute of Technology, China), Tianyu Huang (Harbin Institute of Technology, China), Yukang Ding (Taobao, Alibaba Group, China), Hui Li (Harbin Institute of Technology, China), Wangmeng Zuo (Harbin Institute of Technology, China)