4 citations · 7 across the 17 of their papers we have counts for
18 papers
MMOE: Modernizing Diffusion Transformers with Efficient Expert Design
Yanhao Jia, Jiepeng Wang, Haibin Huang +3
Modern large language models scale successfully by pairing capacity growth with efficiency, keeping per-token and deployment costs under control as capacity grows. AIGC Foundation…
ShotPlan: Cinematic Video Generation with Learnable Planning Token
Su Guo, Guangce Liu, Haosen Yang +7
Current video generation models achieve impressive results in single-shot generation, yet remain limited in cinematic video generation, where coherent narratives and effective mult…
QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning
Yiheng Zhang, Zhe Zhu, Tingrui Shen +11
The generation of production-ready quad-dominant meshes is a cornerstone of modern 3D content creation. Generating anisotropic quad-dominant meshes from point clouds is challenging…
SIFT: Self-Imagination Fine-Tuning for Physically Plausible Motion in Video Diffusion Models
Ruoyu Wang, Jialun Liu, Huayang Huang +5
Recent advances in video diffusion models have greatly improved visual fidelity, yet their generated motions often violate physical plausibility. We observe a common kinematic fail…
TeleWorld: Towards Dynamic Multimodal Synthesis with a 4D World Model
Yabo Chen, Yuanzhi Liang, Jiepeng Wang +24
World models aim to endow AI systems with the ability to represent, generate, and interact with dynamic environments in a coherent and temporally consistent manner. While recent vi…
CtrlVDiff: Controllable Video Generation via Unified Multimodal Video Diffusion
Dianbing Xi, Jiepeng Wang, Yuanzhi Liang +8
We tackle the dual challenges of video understanding and controllable video generation within a unified diffusion framework. Our key insights are two-fold: geometry-only cues (e.g.…
UniModel: A Visual-Only Framework for Unified Multimodal Understanding and Generation
Chi Zhang, Jiepeng Wang, Youming Wang +5
We present UniModel, a unified generative model that jointly supports visual understanding and visual generation within a single pixel-to-pixel diffusion framework. Our goal is to…
Uni-Inter: Unifying 3D Human Motion Synthesis Across Diverse Interaction Contexts
Sheng Liu, Yuanzhi Liang, Jiepeng Wang +3
We present Uni-Inter, a unified framework for human motion generation that supports a wide range of interaction scenarios: including human-human, human-object, and human-scene-with…
Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction
Chenyou Fan, Fangzheng Yan, Chenjia Bai +4
Learning a generalizable bimanual manipulation policy is extremely challenging for embodied agents due to the large action space and the need for coordinated arm movements. Existin…
Metric-Solver: Sliding Anchored Metric Depth Estimation from a Single Image
Tao Wen, Jiepeng Wang, Yabo Chen +3
Accurate and generalizable metric depth estimation is crucial for various computer vision applications but remains challenging due to the diverse depth scales encountered in indoor…
OmniVDiff: Omni Controllable Video Diffusion for Generation and Understanding
Dianbing Xi, Jiepeng Wang, Yuanzhi Liang +5
In this paper, we propose a novel framework for controllable video diffusion, OmniVDiff , aiming to synthesize and comprehend multiple video visual content in a single diffusion mo…
TeethDreamer: 3D Teeth Reconstruction from Five Intra-oral Photographs
Chenfan Xu, Zhentao Liu, Yuan Liu +6
Orthodontic treatment usually requires regular face-to-face examinations to monitor dental conditions of the patients. When in-person diagnosis is not feasible, an alternative is t…
MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors
Qingming Liu, Yuan Liu, Jiepeng Wang +4
In this paper, we propose MoDGS, a new pipeline to render novel views of dy namic scenes from a casually captured monocular video. Previous monocular dynamic NeRF or Gaussian Splat…
ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models
Rui Xu, Jiepeng Wang, Hao Pan +6
In this paper, we study an under-explored but important factor of diffusion generative models, i.e., the combinatorial complexity. Data samples are generally high-dimensional, and…
StructRe: Rewriting for Structured Shape Modeling
Jiepeng Wang, Hao Pan, Yang Liu +3
Man-made 3D shapes are naturally organized in parts and hierarchies; such structures provide important constraints for shape reconstruction and generation. Modeling shape structure…
NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images
Yuan Liu, Peng Wang, Cheng Lin +5
We present a neural rendering-based method called NeRO for reconstructing the geometry and the BRDF of reflective objects from multiview images captured in an unknown environment.…
Batch-based Model Registration for Fast 3D Sherd Reconstruction
Jiepeng Wang, Congyi Zhang, Peng Wang +4
3D reconstruction techniques have widely been used for digital documentation of archaeological fragments. However, efficient digital capture of fragments remains as a challenge. In…
NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors
Jiepeng Wang, Peng Wang, Xiaoxiao Long +4
Reconstructing 3D indoor scenes from 2D images is an important task in many computer vision and graphics applications. A main challenge in this task is that large texture-less area…