activity
20162024
most citedNerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion

37 citations · 186 across the 39 of their papers we have counts for

collaborators

39 papers

cs.CV2024

PocoLoco: A Point Cloud Diffusion Model of Human Shape in Loose Clothing

Siddharth Seth, Rishabh Dabral, Diogo Luvizon +4

Modeling a human avatar that can plausibly deform to articulations is an active area of research. We present PocoLoco -- the first template-free, point-based, pose-conditioned gene…

cs.CV2024

TEDRA: Text-based Editing of Dynamic and Photoreal Actors

Basavaraj Sunagad, Heming Zhu, Mohit Mendiratta +3

Over the past years, significant progress has been made in creating photorealistic and drivable 3D avatars solely from videos of real humans. However, a core remaining challenge is…

cs.CV2024

DiffAge3D: Diffusion-based 3D-aware Face Aging

Junaid Wahid, Fangneng Zhan, Pramod Rao +1

Face aging is the process of converting an individual's appearance to a younger or older version of themselves. Existing face aging techniques have been limited to 2D settings, whi…

cs.CV202413 cited

Lite2Relight: 3D-aware Single Image Portrait Relighting

Pramod Rao, Gereon Fox, Abhimitra Meka +8

Achieving photorealistic 3D view synthesis and relighting of human portraits is pivotal for advancing AR/VR applications. Existing methodologies in portrait relighting demonstrate…

cs.CV2024

Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models

Zhening Xing, Gereon Fox, Yanhong Zeng +4

Large Language Models have shown remarkable efficacy in generating streaming data such as text and audio, thanks to their temporally uni-directional attention mechanism, which mode…

cs.GR2024

Learning Images Across Scales Using Adversarial Training

Krzysztof Wolski, Adarsh Djeacoumar, Alireza Javanmardi +7

The real world exhibits rich structure and detail across many scales of observation. It is difficult, however, to capture and represent a broad spectrum of scales using ordinary im…