4 papers · 1 filter
LoFA: Learning to Predict Personalized Priors for Fast Adaptation of Visual Generative Models
Yiming Hao, Mutian Xu, Chongjie Ye +4
Personalizing visual generative models to meet specific user needs has gained increasing attention, yet current methods like Low-Rank Adaptation (LoRA) remain impractical due to th…
Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data
Ke Fan, Shunlin Lu, Minyue Dai +6
Generating diverse and natural human motion sequences based on textual descriptions constitutes a fundamental and challenging research area within the domains of computer vision, g…
ARMO: Autoregressive Rigging for Multi-Category Objects
Mingze Sun, Shiwei Mao, Keyi Chen +5
Recent advancements in large-scale generative models have significantly improved the quality and diversity of 3D shape generation. However, most existing methods focus primarily on…
MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space
Lixing Xiao, Shunlin Lu, Huaijin Pi +7
This paper addresses the challenge of text-conditioned streaming motion generation, which requires us to predict the next-step human pose based on variable-length historical motion…