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Brian Gordon

4 accepted papers

2025

RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation

EMNLP 2025

Subject-driven text-to-image (T2I) generation aims to produce images that align with a given textual description, while preserving the visual identity from a referenced subject image. Despite its broad downstream applicability—ranging from enhanced personalization in image generation to consistent c

Cited by 0SourcePDFScholar
2023

Human Motion Diffusion Model

ICLR 2023top-25%

Natural and expressive human motion generation is the holy grail of computer animation. It is a challenging task, due to the diversity of possible motion, human perceptual sensitivity to it, and the difficulty of accurately describing it. Therefore, current generative solutions are either low-qualit…

2022

FLEX: Extrinsic Parameters-Free Multi-View 3D Human Motion Reconstruction

ECCV 2022poster

"The increasing availability of video recordings made by multiple cameras has offered new means for mitigating occlusion and depth ambiguities in pose and motion reconstruction methods. Yet, multi-view algorithms strongly depend on camera parameters, particularly on relative transformations between…

2022

MotionCLIP: Exposing Human Motion Generation to CLIP Space

ECCV 2022poster

"We introduce MotionCLIP, a 3D human motion auto-encoder featuring a latent embedding that is disentangled, well behaved, and supports highly semantic textual descriptions. MotionCLIP gains its unique power by aligning its latent space with that of the Contrastive Language-Image Pre-training (CLIP)…