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Vidit Goel

7 accepted papers

2025

Fused View-Time Attention and Feedforward Reconstruction for 4D Scene Generation

Chaoyang Wang, Ashkan Mirzaei, Vidit Goel, Willi Menapace, Aliaksandr Siarohin, Michael Vasilkovsky +5
NeurIPS 2025poster

We propose the first framework capable of computing a 4D spatio-temporal grid of video frames and 3D Gaussian particles for each time step using a feed-forward architecture. Our architecture has two main components, a 4D video model and a 4D reconstruction model. In the first part, we analyze curren…

Cited by 0Source ↗Scholar ↗
2025

Lightweight Predictive 3D Gaussian Splats

Junli Cao, Vidit Goel, Chaoyang Wang, Anil Kag, Ju Hu, Sergei Korolev +3
ICLR 2025poster

Recent approaches representing 3D objects and scenes using Gaussian splats show increased rendering speed across a variety of platforms and devices. While rendering such representations is indeed extremely efficient, storing and transmitting them is often prohibitively expensive. To represent large-…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2025

Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach

Yunuo Chen, Junli Cao, Vidit Goel, Sergei Korolev, Chenfanfu Jiang, Jian Ren +2
NeurIPS 2025poster

We present a novel video generation framework that integrates 3-dimensional geometry and dynamic awareness. To achieve this, we augment 2D videos with 3D point trajectories and align them in pixel space. The resulting 3D-aware video dataset, PointVid, is then used to fine-tune a latent diffusion mod…

Cited by 0Source ↗Scholar ↗
2025

Wonderland: Navigating 3D Scenes from a Single Image

Hanwen Liang, Junli Cao, Vidit Goel, Guocheng Qian, Sergei Korolev, Demetri Terzopoulos +3
CVPR 2025poster

This paper addresses a challenging question: how can we efficiently create high-quality, wide-scope 3D scenes from a single arbitrary image?Existing methods face several constraints, such as requiring multi-view data, time-consuming per-scene optimization, low visual quality, and distorted reconstru…

Cited by 12Source ↗PDF ↗Scholar ↗
2024

OpenBias: Open-set Bias Detection in Text-to-Image Generative Models

Moreno D'Incà, Elia Peruzzo, Massimiliano Mancini, Dejia Xu, Vidit Goel, Xingqian Xu +3
CVPR 2024highlight

Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments it is necessary to deeply investigate their safety and fairness to not disseminate and perpetuate any kind of biases. However existing works focus on de…

Cited by 32Source ↗PDF ↗code ↗Scholar ↗
2024

PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor

Vidit Goel, Elia Peruzzo, Yifan Jiang, Dejia Xu, Xingqian Xu, Nicu Sebe +3
CVPR 2024poster

Generative image editing has recently witnessed extremely fast-paced growth. Some works use high-level conditioning such as text while others use low-level conditioning. Nevertheless most of them lack fine-grained control over the properties of the different objects present in the image i.e. object-…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2022

VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution

Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang +2
CVPR 2022poster

Videos typically record the streaming and continuous visual data as discrete consecutive frames. Since the storage cost is expensive for videos of high fidelity, most of them are stored in a relatively low resolution and frame rate. Recent works of Space-Time Video Super-Resolution (STVSR) are devel…

Cited by 120PDF ↗code ↗Scholar ↗