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While the generation of 3D content from single-view images has been extensively studied, the creation of physically consistent 3D dynamic scenes from videos remains in its early stages. We propose a novel framework leveraging generative 3D…

Graphics · Computer Science 2025-10-31 Zhiwei Zhao , Alan Zhao , Minchen Li , Yixin Hu

We present 4DNeX, the first feed-forward framework for generating 4D (i.e., dynamic 3D) scene representations from a single image. In contrast to existing methods that rely on computationally intensive optimization or require multi-frame…

Computer Vision and Pattern Recognition · Computer Science 2025-08-19 Zhaoxi Chen , Tianqi Liu , Long Zhuo , Jiawei Ren , Zeng Tao , He Zhu , Fangzhou Hong , Liang Pan , Ziwei Liu

Reconstructing dynamic 3D scenes from 2D images and generating diverse views over time is challenging due to scene complexity and temporal dynamics. Despite advancements in neural implicit models, limitations persist: (i) Inadequate Scene…

Computer Vision and Pattern Recognition · Computer Science 2024-02-23 Zeyu Yang , Hongye Yang , Zijie Pan , Li Zhang

Video super-resolution (VSR) approaches have shown impressive temporal consistency in upsampled videos. However, these approaches tend to generate blurrier results than their image counterparts as they are limited in their generative…

Computer Vision and Pattern Recognition · Computer Science 2024-05-03 Yiran Xu , Taesung Park , Richard Zhang , Yang Zhou , Eli Shechtman , Feng Liu , Jia-Bin Huang , Difan Liu

Novel view synthesis of dynamic scenes is fundamental to achieving photorealistic 4D reconstruction and immersive visual experiences. Recent progress in Gaussian-based representations has significantly improved real-time rendering quality,…

Computer Vision and Pattern Recognition · Computer Science 2026-02-06 Zhanfeng Liao , Jiajun Zhang , Hanzhang Tu , Zhixi Wang , Yunqi Gao , Hongwen Zhang , Yebin Liu

Recent progress in driving video generation has shown significant potential for enhancing self-driving systems by providing scalable and controllable training data. Although pretrained state-of-the-art generation models, guided by 2D layout…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Yishen Ji , Ziyue Zhu , Zhenxin Zhu , Kaixin Xiong , Ming Lu , Zhiqi Li , Lijun Zhou , Haiyang Sun , Bing Wang , Tong Lu

Creating 4D fields of Gaussian Splatting from images or videos is a challenging task due to its under-constrained nature. While the optimization can draw photometric reference from the input videos or be regulated by generative models,…

Computer Vision and Pattern Recognition · Computer Science 2024-05-15 Quankai Gao , Qiangeng Xu , Zhe Cao , Ben Mildenhall , Wenchao Ma , Le Chen , Danhang Tang , Ulrich Neumann

3D Gaussian Splatting has recently achieved notable success in novel view synthesis for dynamic scenes and geometry reconstruction in static scenes. Building on these advancements, early methods have been developed for dynamic surface…

Computer Vision and Pattern Recognition · Computer Science 2024-11-12 Decai Chen , Brianne Oberson , Ingo Feldmann , Oliver Schreer , Anna Hilsmann , Peter Eisert

Novel-view synthesis aims to generate novel views of a scene from multiple input images or videos, and recent advancements like 3D Gaussian splatting (3DGS) have achieved notable success in producing photorealistic renderings with efficient…

Computer Vision and Pattern Recognition · Computer Science 2024-10-22 Xi Liu , Chaoyi Zhou , Siyu Huang

Reconstructing dynamic 3D scenes from sparse multi-view videos is highly ill-posed, often leading to geometric collapse, trajectory drift, and floating artifacts. Recent attempts introduce generative priors to hallucinate missing content,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Zhenlong Wu , Zihan Zheng , Xuanxuan Wang , Qianhe Wang , Hua Yang , Xiaoyun Zhang , Qiang Hu , Wenjun Zhang

Synthesizing photo-realistic visual observations from an ego vehicle's driving trajectory is a critical step towards scalable training of self-driving models. Reconstruction-based methods create 3D scenes from driving logs and synthesize…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Jiageng Mao , Boyi Li , Boris Ivanovic , Yuxiao Chen , Yan Wang , Yurong You , Chaowei Xiao , Danfei Xu , Marco Pavone , Yue Wang

Real-time rendering of dynamic scenes with view-dependent effects remains a fundamental challenge in computer graphics. While recent advances in Gaussian Splatting have shown promising results separately handling dynamic scenes (4DGS) and…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Zhongpai Gao , Benjamin Planche , Meng Zheng , Anwesa Choudhuri , Terrence Chen , Ziyan Wu

View-predictive generative models provide strong priors for lifting object-centric images and videos into 3D and 4D through rendering and score distillation objectives. A question then remains: what about lifting complete multi-object…

Computer Vision and Pattern Recognition · Computer Science 2024-05-24 Wen-Hsuan Chu , Lei Ke , Katerina Fragkiadaki

While recent foundational video generators produce visually rich output, they still struggle with appearance drift, where objects gradually degrade or change inconsistently across frames, breaking visual coherence. We hypothesize that this…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Hyeonho Jeong , Chun-Hao Paul Huang , Jong Chul Ye , Niloy Mitra , Duygu Ceylan

Recent 4D reconstruction methods have yielded impressive results but rely on sharp videos as supervision. However, motion blur often occurs in videos due to camera shake and object movement, while existing methods render blurry results when…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Renlong Wu , Zhilu Zhang , Mingyang Chen , Zifei Yan , Wangmeng Zuo

This paper tackles the challenge of recovering 4D dynamic scenes from videos captured by as few as four portable cameras. Learning to model scene dynamics for temporally consistent novel-view rendering is a foundational task in computer…

Computer Vision and Pattern Recognition · Computer Science 2026-04-07 Junsheng Zhou , Zhifan Yang , Liang Han , Wenyuan Zhang , Kanle Shi , Shenkun Xu , Yu-Shen Liu

Reconstructing dynamic 4D scenes from monocular videos is a fundamental yet challenging task. While recent 3D foundation models provide strong geometric priors, their performance significantly degrades in dynamic environments. This…

Computer Vision and Pattern Recognition · Computer Science 2026-05-13 Ying Zang , Xuanyi Liu , Yidong Han , Deyi Ji , Chaotao Ding , Yuanqi Hu , Qi Zhu , Xuanfu Li , Jin Ma , Lingyun Sun , Tianrun Chen , Lanyun Zhu

4D Gaussian Splatting (4DGS) enables high-quality dynamic novel view synthesis, yet current models remain monolithic bitstreams that clients must download in full before any frame can be rendered, causing black-screen waits of tens to…

Computer Vision and Pattern Recognition · Computer Science 2026-05-13 Jiachen Li , Guangzhi Han , Jin Wan , Delong Han , Yuan Gao , Min Li , Mingle Zhou , Gang Li

Although 3D Gaussian Splatting (3D-GS) achieves efficient rendering for novel view synthesis, extending it to dynamic scenes still results in substantial memory overhead from replicating Gaussians across frames. To address this challenge,…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Chun-Tin Wu , Jun-Cheng Chen

We introduce PortraitGen, a powerful portrait video editing method that achieves consistent and expressive stylization with multimodal prompts. Traditional portrait video editing methods often struggle with 3D and temporal consistency, and…

Computer Vision and Pattern Recognition · Computer Science 2024-09-23 Xuan Gao , Haiyao Xiao , Chenglai Zhong , Shimin Hu , Yudong Guo , Juyong Zhang