Publications (6)
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation
Aviv Slobodkin, Hagai Taitelbaum, Yonatan Bitton +7
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 ima…
Human Motion Diffusion Model
Guy Tevet, Sigal Raab, Brian Gordon +3
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 sensitiv…
Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment
Brian Gordon, Yonatan Bitton, Yonatan Shafir +5
While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment. In this paper, we present a metho…
Unblocking Fine-Grained Evaluation of Detailed Captions: An Explaining AutoRater and Critic-and-Revise Pipeline
Brian Gordon, Yonatan Bitton, Andreea Marzoca +4
Large Vision-Language Models (VLMs) now generate highly detailed, paragraphlength image captions, yet evaluating their factual accuracy remains challenging. Current methods often m…
FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction
Brian Gordon, Sigal Raab, Guy Azov +2
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 meth…
MotionCLIP: Exposing Human Motion Generation to CLIP Space
Guy Tevet, Brian Gordon, Amir Hertz +2
We introduce MotionCLIP, a 3D human motion auto-encoder featuring a latent embedding that is disentangled, well behaved, and supports highly semantic textual descriptions. MotionCL…