2 citations · 5 across the 21 of their papers we have counts for
25 papers
World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video
Liyuan Zhu, Shengyu Huang, Amrita Mazumdar +6
We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos. Our approach conditions a video model on dense, p…
ReGenHuman: Re-Generating Human Appearances for Realistic Full-Body Video Anonymization
Adam Sun, Eshaan Barkataki, Arnold Milstein +2
Anonymizing human-centric video data is an understudied problem. Prior anonymization techniques either blur or redact pixels at the cost of realism and downstream utility, or gener…
Policy-based Foveated Imaging and Perception
Howard Xiao, Jan Ackermann, Boyang Deng +1
Ultra-high-resolution image sensors offer the potential to capture fine spatial details critical for many visual perception tasks, but acquiring and processing all pixels at full r…
Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis
Zhengfei Kuang, Adam Sun, Liyuan Zhu +7
Modern vehicle platforms are equipped with a rich sensor suite, including LiDAR, calibrated multi-camera rigs, and accurate ego-motion, that in principle offers strong signal for r…
GaussFusion: Improving 3D Reconstruction in the Wild with A Geometry-Informed Video Generator
Liyuan Zhu, Manjunath Narayana, Michal Stary +3
We present GaussFusion, a novel approach for improving 3D Gaussian splatting (3DGS) reconstructions in the wild through geometry-informed video generation. GaussFusion mitigates co…
Infinite Gaze Generation for Videos with Autoregressive Diffusion
Jenna Kang, Colin Groth, Tong Wu +4
Predicting human gaze in video is fundamental to advancing scene understanding and multimodal interaction. While traditional saliency maps provide spatial probability distributions…
Image2Garment: Simulation-ready Garment Generation from a Single Image
Selim Emir Can, Jan Ackermann, Kiyohiro Nakayama +7
Estimating physically accurate, simulation-ready garments from a single image is challenging due to the absence of image-to-physics datasets and the ill-posed nature of this proble…
BAgger: Backwards Aggregation for Mitigating Drift in Autoregressive Video Diffusion Models
Ryan Po, Eric Ryan Chan, Changan Chen +1
Autoregressive video models are promising for world modeling via next-frame prediction, but they suffer from exposure bias: a mismatch between training on clean contexts and infere…
BulletTime: Decoupled Control of Time and Camera Pose for Video Generation
Yiming Wang, Qihang Zhang, Shengqu Cai +7
Emerging video diffusion models achieve high visual fidelity but fundamentally couple scene dynamics with camera motion, limiting their ability to provide precise spatial and tempo…
Taming Flow-based I2V Models for Creative Video Editing
Xianghao Kong, Hansheng Chen, Yuwei Guo +4
Although image editing techniques have advanced significantly, video editing, which aims to manipulate videos according to user intent, remains an emerging challenge. Most existing…
Mixture of Contexts for Long Video Generation
Shengqu Cai, Ceyuan Yang, Lvmin Zhang +10
Long video generation is fundamentally a long context memory problem: models must retain and retrieve salient events across a long range without collapsing or drifting. However, sc…
Towards Vision-Language-Garment Models for Web Knowledge Garment Understanding and Generation
Jan Ackermann, Kiyohiro Nakayama, Guandao Yang +2
Multimodal foundation models have demonstrated strong generalization, yet their ability to transfer knowledge to specialized domains such as garment generation remains underexplore…
Video World Models with Long-term Spatial Memory
Tong Wu, Shuai Yang, Ryan Po +4
Emerging world models autoregressively generate video frames in response to actions, such as camera movements and text prompts, among other control signals. Due to limited temporal…
Long-Context State-Space Video World Models
Ryan Po, Yotam Nitzan, Richard Zhang +5
Video diffusion models have recently shown promise for world modeling through autoregressive frame prediction conditioned on actions. However, they struggle to maintain long-term m…
Interspatial Attention for Efficient 4D Human Video Generation
Ruizhi Shao, Yinghao Xu, Yujun Shen +5
Generating photorealistic videos of digital humans in a controllable manner is crucial for a plethora of applications. Existing approaches either build on methods that employ templ…
GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography
Mengchen Zhang, Tong Wu, Jing Tan +3
Camera trajectory design plays a crucial role in video production, serving as a fundamental tool for conveying directorial intent and enhancing visual storytelling. In cinematograp…
GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling
Yang Zheng, Menglei Chai, Delio Vicini +5
We present GroomLight, a novel method for relightable hair appearance modeling from multi-view images. Existing hair capture methods struggle to balance photorealistic rendering wi…
CameraCtrl II: Dynamic Scene Exploration via Camera-controlled Video Diffusion Models
Hao He, Ceyuan Yang, Shanchuan Lin +7
This paper introduces CameraCtrl II, a framework that enables large-scale dynamic scene exploration through a camera-controlled video diffusion model. Previous camera-conditioned v…
3DGen-Bench: Comprehensive Benchmark Suite for 3D Generative Models
Yuhan Zhang, Mengchen Zhang, Tong Wu +4
3D generation is experiencing rapid advancements, while the development of 3D evaluation has not kept pace. How to keep automatic evaluation equitably aligned with human perception…
FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views
Shangzhan Zhang, Jianyuan Wang, Yinghao Xu +5
We present FLARE, a feed-forward model designed to infer high-quality camera poses and 3D geometry from uncalibrated sparse-view images (i.e., as few as 2-8 inputs), which is a cha…
RelightVid: Temporal-Consistent Diffusion Model for Video Relighting
Ye Fang, Zeyi Sun, Shangzhan Zhang +6
Diffusion models have demonstrated remarkable success in image generation and editing, with recent advancements enabling albedo-preserving image relighting. However, applying these…
FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models
Tong Wu, Yinghao Xu, Ryan Po +6
Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications. However, accurately describing desired visual attributes can…
AIpparel: A Multimodal Foundation Model for Digital Garments
Kiyohiro Nakayama, Jan Ackermann, Timur Levent Kesdogan +6
Apparel is essential to human life, offering protection, mirroring cultural identities, and showcasing personal style. Yet, the creation of garments remains a time-consuming proces…
LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation
Shuai Yang, Jing Tan, Mengchen Zhang +5
3D immersive scene generation is a challenging yet critical task in computer vision and graphics. A desired virtual 3D scene should 1) exhibit omnidirectional view consistency, and…