1 citations · 1 across the 13 of their papers we have counts for
15 papers
Video Models as Native 4D Renderers: World-Grounded Conditioning from Animated Mesh
Junhao Chen, Mingjin Chen, Henghaofan Zhang +9
Pretrained video diffusion models can act as renderers when the desired scene state is already specified by an animated mesh, a camera trajectory, and a reference image. This 4D ge…
ViP-Rig: Visual-Prompted Controllable Rigging
Zihan Qin, Mingze Sun, Yifan Mao +6
Rigging is inherently task-dependent because the same mesh may require different skeletons and deformation behaviors across animation tasks. In practice, artists often inspect an i…
TG-Diff: Coupling Discrete Topology Diffusion and Topology-conditioned Geometry Diffusions for B-Rep Generation
MingZe Sun, Haiyong Jiang, Bingchen Yang +4
Boundary representation (B-rep) is the standard format for computer-aided design (CAD). This article proposes a lightweight two-stage diffusion-based B-rep generation framework, TG…
4DThinker: Thinking with 4D Imagery for Dynamic Spatial Understanding
Zhangquan Chen, Manyuan Zhang, Xinlei Yu +9
Dynamic spatial reasoning from monocular video is essential for bridging visual intelligence and the physical world, yet remains challenging for vision-language models (VLMs). Prio…
Animator-Centric Skeleton Generation on Objects with Fine-Grained Details
Mingze Sun, Cheng Zeng, Jiansong Pei +7
Skeleton generation is essential for animating 3D assets, but current deep learning methods remain limited: they cannot handle the growing structural complexity of modern models an…
Topology-Agnostic Animal Motion Generation from Text Prompt
Keyi Chen, Mingze Sun, Zhenyu Liu +2
Motion generation is fundamental to computer animation and widely used across entertainment, robotics, and virtual environments. While recent methods achieve impressive results, mo…
Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views
Zhangquan Chen, Manyuan Zhang, Xinlei Yu +8
Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limit…
SIFThinker: Spatially-Aware Image Focus for Visual Reasoning
Zhangquan Chen, Ruihui Zhao, Chuwei Luo +4
Current multimodal large language models (MLLMs) still face significant challenges in complex visual tasks (e.g., spatial understanding, fine-grained perception). Prior methods hav…
DanceTogether! Identity-Preserving Multi-Person Interactive Video Generation
Junhao Chen, Mingjin Chen, Jianjin Xu +9
Controllable video generation (CVG) has advanced rapidly, yet current systems falter when more than one actor must move, interact, and exchange positions under noisy control signal…
NFR: Neural Feature-Guided Non-Rigid Shape Registration
Zhangquan Chen, Puhua Jiang, Mingze Sun +1
In this paper, we propose a novel learning-based framework for 3D shape registration, which overcomes the challenges of significant non-rigid deformation and partiality undergoing…
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…
DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters
Mingze Sun, Junhao Chen, Junting Dong +7
Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal. However, animating these generated characters remains a challenging tas…
SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation
Mingze Sun, Chen Guo, Puhua Jiang +3
In this paper, we propose SRIF, a novel Semantic shape Registration framework based on diffusion-based Image morphing and Flow estimation. More concretely, given a pair of extrinsi…
Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony
Chao Xu, Mingze Sun, Zhi-Qi Cheng +5
In this paper, we propose a novel framework, Combo, for harmonious co-speech holistic 3D human motion generation and efficient customizable adaption. In particular, we identify tha…
Non-Rigid Shape Registration via Deep Functional Maps Prior
Puhua Jiang, Mingze Sun, Ruqi Huang
In this paper, we propose a learning-based framework for non-rigid shape registration without correspondence supervision. Traditional shape registration techniques typically rely o…