Dynamic Novel View Synthesis

Follow this topic Ask about this topic

Momentum

1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 26

All topics

DyRAD: Radar Novel View Synthesis for Dynamic Driving Scenes
Sep 30, 2026Merav Keidar, Tomer Borreda, Rajalakshmi Nandakumar +1Millimeter-Wave Radar mmWave Radar Sensing
Technion · Cornell Tech · NVIDIA
Stabilizing Camera-Controlled Novel View Synthesis at Inference Time
Sep 3, 2026Prajwal Singh, Arjun Badola, Seema Kumari +2Camera-Controlled Video Generation Video Diffusion Models
CVIG Lab and ISLab, D3 Center · Osaka University
CC-4DGS: Computational Deformation and Point-Cloud Compression for Storage-Efficient Dynamic Gaussian Splatting
Sep 2, 2026Kyungdae Park, Chae Eun RheeGaussian Splatting Dynamic Novel View Synthesis
Department of Artificial Intelligence Semiconductor Engineering, Hanyang University, Seoul 04763, South Korea · Department of Electronic Engineering, Hanyang University, Seoul 04763, South Korea
MV2: Multi-View Multi-Vehicle Driving Dataset for Novel View Synthesis
Aug 12, 2026Sanjay Bhargav Dharavath, Hanvitha Saraswathi Mukkamala, Faizan Farooq Khan +4Autonomous Driving Datasets Autonomous Driving Benchmarks
International Institute of Information Technology, Hyderabad · King Abdullah University of Science and Technology (KAUST) · IIT, National Centre for Scientific Research “Demokritos” +2
FAST-GS: Frequency Aware Space-time Gaussian Splatting for Photorealistic Dynamic Novel View Synthesis
Aug 3, 2026Zhengyang Zhang, Ziyu Lu, PengCheng Li +63D Gaussian Splatting 4D Reconstruction
1Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Central Media Technology Institute, Huawei
D^2-4DGS: Dual-Depth Guided Sparse-Camera 4D Gaussian Splatting
Aug 3, 2026Jijian Zhao3D Gaussian Splatting Dynamic Scene Reconstruction
Huazhong University of Science and Technology
GrainGS: Gradient-Decoupled Gaussian Splatting for Efficient Dynamic Novel View Synthesis
Jul 23, 2026Jiahao He, Yihua Shao, Zhengkai Zhao +63D Gaussian Splatting Dynamic Scene Reconstruction
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China · Department of Computing, Hong Kong Polytechnic University, Hong Kong, China +4
PE-Field 4D: Video Generation Models as Canvas
Jul 17, 2026Yunpeng Bai, Haoxiang Li, Qixing HuangCamera-Controlled Video Generation Diffusion Transformer
University of Texas at Austin, USA · Pixocial Technology, USA
Online Neural Space Time Memory for Dynamic Novel View Synthesis
Jul 16, 2026Baback Elmieh, Lynn Tsai, Zeman Li +8Memory-Augmented Neural Networks Dynamic Novel View Synthesis
University of Washington · Google
4D Human-Scene Reconstruction from Low-Overlap Captures
Jul 10, 2026Minhyuk Hwang, Sangmin Kim, Seunguk Do +24D Reconstruction Dynamic Scene Reconstruction
Seoul National University, Republic of Korea
On the Design of Mixture-of-Experts for Dynamic Gaussian Splatting
Jul 9, 2026In-Hwan Jin, Hyeongju Mun, Joonsoo Kim +23D Gaussian Splatting Dynamic Scene Reconstruction
Department of Electrical and Electronics Engineering, Pusan National University, Busan, Republic of Korea · Immersive Media Research Section, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea · Department of Electrical and Electronics Engineering and JYS AI Nexus Institute, Pusan National University, Busan, Republic of Korea
NeoMap: Training-free Novel-View Synthesis from Single Images and Videos
Jul 2, 2026Jinxi Li, Tianyi Zhang, Yafei Yang +4Multi-View Consistency Video Diffusion Models
Shenzhen Research Institute, The Hong Kong Polytechnic University · vLAR Group, The Hong Kong Polytechnic University
SPECSIA: Stylization Dataset for Novel-View Enhancement in Drawing-based 3D Animation
Jul 1, 2026Kyuwon Kim, Sunjae Yoon, Chang D. YooImage Enhancement Dynamic Novel View Synthesis
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea · Department of AI, Chung-Ang University, Seoul, Republic of Korea
Envision4D: Envisioning Visual Futures via Feed-forward 4D Gaussian Splatting for Autonomous Driving
Jun 9, 2026Qi Song, Yifei He, Chi Zhang +63D Gaussian Splatting Autonomous Driving
Tsinghua University · The Chinese University of Hong Kong, Shenzhen
Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis
Jun 1, 2026Zhengfei Kuang, Adam Sun, Liyuan Zhu +7Camera-Conditioned Video Generation Video Diffusion Models
1Stanford Univerity · 2NVIDIA
DSD-GS: Dynamic-Static Decomposition of Gaussian Splatting for Efficient and High-Fidelity Dynamic Scene Reconstruction
May 29, 2026Youngtae Han, Sung-hwan Han, Youngmin Yi3D Gaussian Splatting Dynamic Scene Reconstruction
Department of Artificial Intelligence Engineering, Sogang University
DEVIS-GRPO: Unleashing GRPO on Dynamic Extreme View Synthesis
May 16, 2026Yi Zuo, Huimin Wu, Lingling Li +3Camera-Controlled Video Generation Dynamic Novel View Synthesis
1Xidian University · 2State Key Laboratory of General Artificial Intelligence, BIGAI
3D Gaussian Splatting for Efficient Retrospective Dynamic Scene Novel View Synthesis with a Standardized Benchmark
May 12, 2026Yunxiao Zhang, Suryansh Kumar3D Gaussian Splatting Multi-View 3D Reconstruction
Visual and Spatial AI Lab1, VCCM Section College of PVFA2, Department of ECEN3, Department of CSCE4, Texas A&M University, College Station, Texas, USA
MoCam: Unified Novel View Synthesis via Structured Denoising Dynamics
May 12, 2026Haofeng Liu, Yang Zhou, Ziheng Wang +6Diffusion Model Sampling Dynamic Novel View Synthesis
PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming
May 12, 2026Jiachen Li, Guangzhi Han, Jin Wan +53D Gaussian Splatting Dynamic Novel View Synthesis
Qilu University of Technology
RealCam: Real-Time Novel-View Video Generation with Interactive Camera Control
May 7, 2026Youcan Xu, Jiaxin Shi, Zhen Wang +5Camera-Controlled Video Generation Real-Time Interactive Video Generation
Zhejiang University, China · Xmax.AI Ltd., China · Hong Kong University of Science and Technology, China
Embody4D: A Generalist Data Engine for Embodied 4D World Modeling
May 3, 2026Peiyan Tu, Hanxin Zhu, Jingwen Sun +6Camera-Conditioned Video Generation Synthetic Data Generation for Robotics
Zhejiang University · University of Science and Technology of China · Beijing Zhongguancun Academy +3
Reshoot-Anything: A Self-Supervised Model for In-the-Wild Video Reshooting
Apr 23, 2026Avinash Paliwal, Adithya Iyer, Shivin Yadav +2Camera-Controlled Video Generation Video Diffusion Models
Morphic Inc.
EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis
Jan 22, 2026Sheng Miao, Sijin Li, Pan Wang +53D Gaussian Splatting Dynamic Scene Reconstruction
Zhejiang University, China. · Huawei, China. · University of Tübingen, Germany.
InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem
Dec 5, 2025Yeobin Hong, Suhyeon Lee, Hyungjin Chung +1Camera-Controlled Video Generation Video Diffusion Models
Graduate School of AI, KAIST, South Korea · EverEx, South Korea · Korea University, South Korea
AnyView: Synthesizing Any Novel View in Dynamic Scenes
Date pendingBasile Van Hoorick, Dian Chen, Shun Iwase +7Multi-View Consistency Camera-Conditioned Video Generation
Toyota Research Institute, CA, USA · Amazon Web Services, CA, USA

Sep 30, 2026, cs.CVJ/K move · Enter open · S save

DyRAD: Radar Novel View Synthesis for Dynamic Driving Scenes

Merav Keidar, Tomer Borreda, Rajalakshmi Nandakumar, Or Litany

Technion · Cornell Tech · NVIDIA

Millimeter-Wave Radar mmWave Radar Sensing Dynamic Scene Reconstruction Dynamic Novel View Synthesis Novel View Synthesis Autonomous Driving Simulation

Open paper AskPDF arXiv

Reconstructing dynamic driving scenes from recorded sensor data supports closed-loop evaluation of autonomous driving systems by synthesizing observations beyond the original trajectory. Unlike cameras and LiDAR, radar measures radial velocity directly through Doppler. Yet existing radar novel-view synthesis fails to exploit this capability: methods addressing dynamic scenes reconstruct only range-azimuth tensors, while methods that render Doppler assume static scenes. Moreover, because radar processing spreads each reflection across multiple bins, existing representations absorb this spread into scene geometry, causing it to render incorrectly when the viewpoint moves. We present DyRAD, which models dynamic driving scenes using static background reflectors and motion-tracked dynamic point reflectors to render complete range-azimuth-Doppler (RAD) tensors. Reflector velocities are derived from object tracks and projected onto the line of sight, making Doppler both a rendered output and supervision for those tracks. Crucially, we render reflectors through a fixed analytic point-spread function (PSF) derived from the radar's signal-processing chain, preventing sensor-induced spread from being baked into the scene representation. Beyond improving scene reconstruction, this separation also enables zero-shot sensor-configuration transfer, allowing the same reconstructed scene to be rendered under different radar specifications without refitting. We evaluate DyRAD on RADIal, Boreas, and a synthetic benchmark across both on-path poses and displaced viewpoints untested by prior work. On RADIal, DyRAD recovers radar detections in 90.7% of reference-detected objects, compared with 26.9% for the strongest baseline.

Momentum

Related topics

Latest papers 26