14 citations · 25 across the 11 of their papers we have counts for
12 papers
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
Kimodo: Scaling Controllable Human Motion Generation
Davis Rempe, Mathis Petrovich, Ye Yuan +21
High-quality human motion data is becoming increasingly important for applications in robotics, simulation, and entertainment. Recent generative models offer a potential data sourc…
Audio2Face-3D: Audio-driven Realistic Facial Animation For Digital Avatars
NVIDIA, :, Chaeyeon Chung +6
Audio-driven facial animation presents an effective solution for animating digital avatars. In this paper, we detail the technical aspects of NVIDIA Audio2Face-3D, including data a…
Fair Generation without Unfair Distortions: Debiasing Text-to-Image Generation with Entanglement-Free Attention
Jeonghoon Park, Juyoung Lee, Chaeyeon Chung +3
Recent advancements in diffusion-based text-to-image (T2I) models have enabled the generation of high-quality and photorealistic images from text. However, they often exhibit socie…
What to Preserve and What to Transfer: Faithful, Identity-Preserving Diffusion-based Hairstyle Transfer
Chaeyeon Chung, Sunghyun Park, Jeongho Kim +1
Hairstyle transfer is a challenging task in the image editing field that modifies the hairstyle of a given face image while preserving its other appearance and background features.…
Enhancing Intrinsic Features for Debiasing via Investigating Class-Discerning Common Attributes in Bias-Contrastive Pair
Jeonghoon Park, Chaeyeon Chung, Juyoung Lee +1
In the image classification task, deep neural networks frequently rely on bias attributes that are spuriously correlated with a target class in the presence of dataset bias, result…
Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction
Chaeyeon Chung, Yeojeong Park, Seunghwan Choi +2
Video-to-video translation aims to generate video frames of a target domain from an input video. Despite its usefulness, the existing networks require enormous computations, necess…
DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation
Bum Chul Kwon, Jungsoo Lee, Chaeyeon Chung +3
Image classification models often learn to predict a class based on irrelevant co-occurrences between input features and an output class in training data. We call the unwanted corr…
Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment
Taewoo Kim, Chaeyeon Chung, Yoonseo Kim +3
Editing hairstyle is unique and challenging due to the complexity and delicacy of hairstyle. Although recent approaches significantly improved the hair details, these models often…
HairFIT: Pose-Invariant Hairstyle Transfer via Flow-based Hair Alignment and Semantic-Region-Aware Inpainting
Chaeyeon Chung, Taewoo Kim, Hyelin Nam +4
Hairstyle transfer is the task of modifying a source hairstyle to a target one. Although recent hairstyle transfer models can reflect the delicate features of hairstyles, they stil…
3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations
Minsoo Lee, Chaeyeon Chung, Hojun Cho +4
While NeRF-based 3D-aware image generation methods enable viewpoint control, limitations still remain to be adopted to various 3D applications. Due to their view-dependent and ligh…
K-Hairstyle: A Large-scale Korean Hairstyle Dataset for Virtual Hair Editing and Hairstyle Classification
Taewoo Kim, Chaeyeon Chung, Sunghyun Park +5
The hair and beauty industry is a fast-growing industry. This led to the development of various applications, such as virtual hair dyeing or hairstyle transfer, to satisfy the cust…