6 papers
Pose-Aware Diffusion for 3D Generation
Zihan Zhou, Luxi Chen, Jingzhi Zhou +4
Generating pose-aligned 3D objects is challenging due to the spatial mismatches and transformation ambiguities inherent in decoupled canonical-then-rotate paradigms. To this end, w…
Bringing Your Portrait to 3D Presence
Jiawei Zhang, Lei Chu, Jiahao Li +6
We present a unified framework for reconstructing animatable 3D human avatars from a single portrait across head, half-body, and full-body inputs. Our method tackles three bottlene…
LLaDA-MoE: A Sparse MoE Diffusion Language Model
Fengqi Zhu, Zebin You, Yipeng Xing +23
We introduce LLaDA-MoE, a large language diffusion model with the Mixture-of-Experts (MoE) architecture, trained from scratch on approximately 20T tokens. LLaDA-MoE achieves compet…
LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning
Zebin You, Shen Nie, Xiaolu Zhang +5
In this work, we introduce LLaDA-V, a purely diffusion-based Multimodal Large Language Model (MLLM) that integrates visual instruction tuning with masked diffusion models, represen…
FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis
Luxi Chen, Zihan Zhou, Min Zhao +6
Generating flexible-view 3D scenes, including 360° rotation and zooming, from single images is challenging due to a lack of 3D data. To this end, we introduce FlexWorld, a novel fr…
Concat-ID: Towards Universal Identity-Preserving Video Synthesis
Yong Zhong, Zhuoyi Yang, Jiayan Teng +2
We present Concat-ID, a unified framework for identity-preserving video generation. Concat-ID employs variational autoencoders to extract image features, which are then concatenate…