A single transformer model jointly predicts depth and normalized canonical coordinates to deliver state-of-the-art 4D facial geometry and tracking with 3x lower correspondence error and 16% better depth accuracy.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
InstantID enables zero-shot identity-preserving image generation from one facial image via a novel IdentityNet that combines strong semantic and weak spatial conditioning with text prompts in diffusion models.
citing papers explorer
-
Face Anything: 4D Face Reconstruction from Any Image Sequence
A single transformer model jointly predicts depth and normalized canonical coordinates to deliver state-of-the-art 4D facial geometry and tracking with 3x lower correspondence error and 16% better depth accuracy.
-
InstantID: Zero-shot Identity-Preserving Generation in Seconds
InstantID enables zero-shot identity-preserving image generation from one facial image via a novel IdentityNet that combines strong semantic and weak spatial conditioning with text prompts in diffusion models.