4 citations · 10 across the 16 of their papers we have counts for
18 papers
FADE: Frame-Aware Diffusion-Transformer-based Multi-Concept Erasure for Video Unlearning
Yuchen Li, Kaiyuan Deng, Chaoran Feng +2
Text-to-video (T2V) diffusion models can reproduce copyrighted, violent, or explicit content, which motivates concept erasure: removing designated concepts from a pretrained model…
WorldCrafter: Consistent Video World Model with Implicit 3D-aware Memory
Wangbo Yu, Kunhao Liu, Wenbo Hu +8
Video world models enable interactive exploration of dynamic environments, yet struggle to respect prior observations over long horizons and across viewpoints. We present WorldCraf…
UniWorld-View: Large-Baseline View Synthesis via Video Diffusion Models
Haiyang Zhou, Wangbo Yu, Chaoran Feng +3
The abundance of casually captured monocular videos and images on social media provides a valuable source for immersive content creation, where generating novel views from such spa…
Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence
Shuailei Ma, Jiaqi Liao, Xinyang Wang +24
Despite the recent promise in robot control, video generative models suffer from a domain mismatch due to their primary focus on content creation. For example, their design inheren…
DeblurNVS: Geometric Latent Diffusion for Novel View Synthesis from Sparse Motion-Blurred Images
Changyue Shi, Wangbo Yu, Chaoran Feng +1
Novel view synthesis (NVS) is a fundamental problem in computer vision and graphics. Recent advances in neural radiance fields (NeRF), 3D Gaussian Splatting (3DGS), and generative…
Enhancing Spatial Understanding in Image Generation via Reward Modeling
Zhenyu Tang, Chaoran Feng, Yufan Deng +5
Recent progress in text-to-image generation has greatly advanced visual fidelity and creativity, but it has also imposed higher demands on prompt complexity-particularly in encodin…
Tune-Your-Style: Intensity-tunable 3D Style Transfer with Gaussian Splatting
Yian Zhao, Rushi Ye, Ruochong Zheng +6
3D style transfer refers to the artistic stylization of 3D assets based on reference style images. Recently, 3DGS-based stylization methods have drawn considerable attention, prima…
Breaking the Vicious Cycle: Coherent 3D Gaussian Splatting from Sparse and Motion-Blurred Views
Zhankuo Xu, Chaoran Feng, Yingtao Li +5
3D Gaussian Splatting (3DGS) has emerged as a state-of-the-art method for novel view synthesis. However, its performance heavily relies on dense, high-quality input imagery, an ass…
Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward
Yuwei Niu, Weiyang Jin, Jiaqi Liao +7
Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation in Unified Multimodal…
E-4DGS: High-Fidelity Dynamic Reconstruction from the Multi-view Event Cameras
Chaoran Feng, Zhenyu Tang, Wangbo Yu +5
Novel view synthesis and 4D reconstruction techniques predominantly rely on RGB cameras, thereby inheriting inherent limitations such as the dependence on adequate lighting, suscep…
GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation
Yuchen Li, Chaoran Feng, Zhenyu Tang +4
We introduce GS2E (Gaussian Splatting to Event), a large-scale synthetic event dataset for high-fidelity event vision tasks, captured from real-world sparse multi-view RGB images.…
Kongzi: A Historical Large Language Model with Fact Enhancement
Jiashu Yang, Ningning Wang, Yian Zhao +6
Large Language Models (LLMs) can exhibit factual inaccuracies during long-chain reasoning or under misleading prompts, limiting their reliability in historical analysis. We propose…
NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact 3D Representations
Zhenyu Tang, Chaoran Feng, Xinhua Cheng +6
3D Gaussian Splatting (3DGS) achieves impressive quality and rendering speed, but with millions of 3D Gaussians and significant storage and transmission costs. In this paper, we ai…
WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
Yuwei Niu, Munan Ning, Mengren Zheng +9
Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evaluation standards predominantly focus on…
AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal Conditions and Larger Scene
Chaoran Feng, Wangbo Yu, Xinhua Cheng +4
Compared to frame-based methods, computational neuromorphic imaging using event cameras offers significant advantages, such as minimal motion blur, enhanced temporal resolution, an…
Next Patch Prediction for Autoregressive Visual Generation
Yatian Pang, Peng Jin, Shuo Yang +8
Autoregressive models, built based on the Next Token Prediction (NTP) paradigm, show great potential in developing a unified framework that integrates both language and vision task…
Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle
Zhenyu Tang, Junwu Zhang, Xinhua Cheng +5
Recent 3D large reconstruction models typically employ a two-stage process, including first generate multi-view images by a multi-view diffusion model, and then utilize a feed-forw…
EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images
Wangbo Yu, Chaoran Feng, Jiye Tang +6
3D Gaussian Splatting (3D-GS) has demonstrated exceptional capabilities in 3D scene reconstruction and novel view synthesis. However, its training heavily depends on high-quality,…