4 citations · 14 across the 17 of their papers we have counts for
20 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…
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…
MSVS-VAE: Multi-Scale Anchored VecSet for High-Fidelity 3D Reconstruction
Dehao Hao, Kaiyi Zhang, Tanghui Jia +10
High-fidelity 3D generative modeling increasingly relies on the latent diffusion paradigm, where the reconstruction quality of the underlying 3D VAE becomes a primary bottleneck. E…
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…
AnyAct: Towards Human Reenactment of Character Motion From Video
Liuhan Chen, Lei Zhong, Jiawei Wang +5
We study the problem of directly deriving an initial human reenactment from a monocular video of a non-human character. Our goal is not to reconstruct the source character itself b…
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…
UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement
Tanghui Jia, Dongyu Yan, Dehao Hao +11
In this report, we introduce UltraShape 1.0, a scalable 3D diffusion framework for high-fidelity 3D geometry generation. The proposed approach adopts a two-stage generation pipelin…
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…
Epona: Autoregressive Diffusion World Model for Autonomous Driving
Kaiwen Zhang, Zhenyu Tang, Xiaotao Hu +9
Diffusion models have demonstrated exceptional visual quality in video generation, making them promising for autonomous driving world modeling. However, existing video diffusion-ba…
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.…
HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene Generation
Haiyang Zhou, Wangbo Yu, Jiawen Guan +3
The rapid advancement of diffusion models holds the promise of revolutionizing the application of VR and AR technologies, which typically require scene-level 4D assets for user exp…
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…
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…
RoomPainter: View-Integrated Diffusion for Consistent Indoor Scene Texturing
Zhipeng Huang, Wangbo Yu, Xinhua Cheng +5
Indoor scene texture synthesis has garnered significant interest due to its important potential applications in virtual reality, digital media and creative arts. Existing diffusion…
ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
Wangbo Yu, Jinbo Xing, Li Yuan +7
Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. In this work, we propose \textbf{ViewCra…
HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions
Haiyang Zhou, Xinhua Cheng, Wangbo Yu +2
3D scene generation is in high demand across various domains, including virtual reality, gaming, and the film industry. Owing to the powerful generative capabilities of text-to-ima…
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,…
Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting
Junwu Zhang, Zhenyu Tang, Yatian Pang +5
Recent one image to 3D generation methods commonly adopt Score Distillation Sampling (SDS). Despite the impressive results, there are multiple deficiencies including multi-view inc…
Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts
Xinhua Cheng, Tianyu Yang, Jianan Wang +4
Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies. However, current me…
HiFi-123: Towards High-fidelity One Image to 3D Content Generation
Wangbo Yu, Li Yuan, Yan-Pei Cao +6
Recent advances in diffusion models have enabled 3D generation from a single image. However, current methods often produce suboptimal results for novel views, with blurred textures…