FaceCLIPNeRF: Text-driven 3D Face Manipulation using Deformable Neural
  Radiance Fields
As recent advances in Neural Radiance Fields (NeRF) have enabled
high-fidelity 3D face reconstruction and novel view synthesis, its manipulation
also became an essential task in 3D vision. However, existing manipulation
methods require extensive human labor, such as a user-provided semantic mask
and manual attribute search unsuitable for non-expert users. Instead, our
approach is designed to require a single text to manipulate a face
reconstructed with NeRF. To do so, we first train a scene manipulator, a latent
code-conditional deformable NeRF, over a dynamic scene to control a face
deformation using the latent code. However, representing a scene deformation
with a single latent code is unfavorable for compositing local deformations
observed in different instances. As so, our proposed Position-conditional
Anchor Compositor (PAC) learns to represent a manipulated scene with spatially
varying latent codes. Their renderings with the scene manipulator are then
optimized to yield high cosine similarity to a target text in CLIP embedding
space for text-driven manipulation. To the best of our knowledge, our approach
is the first to address the text-driven manipulation of a face reconstructed
with NeRF. Extensive results, comparisons, and ablation studies demonstrate the
effectiveness of our approach.
64be087f76a6e2efccb80b0c
Sungwon Hwang
admin_assigned
2023-07-24T07:42:16.208Z
642fcfc0a043f0ac7deeaae0
/avatars/6cc46dd480cdc0d86c7a509e22782a13.svg
Sungwon Hwang
user
sungwon95
64be087f76a6e2efccb80b0d
Junha Hyung
admin_assigned
2023-07-24T07:47:54.356Z
6331ed964db0a767bbee4706
/avatars/2ef3fa6083ca5208f4b87c91345267d9.svg
Junha Hyung
user
JunhaH
64be087f76a6e2efccb80b0e
Daejin Kim
64be087f76a6e2efccb80b0f
Min-Jung Kim
64be087f76a6e2efccb80b10
Jaegul Choo
claimed_verified
2023-07-24T11:31:32.437Z
64be3127805e5b64572da65c
/avatars/edd3e94ba9e375827cc75b164602bcac.svg
Jaegul Choo
user
joyfull78
FaceCLIPNeRF: Text-driven 3D Face Manipulation using Deformable Neural
  Radiance Fields
As recent advances in Neural Radiance Fields (NeRF) have enabled
high-fidelity 3D face reconstruction and novel view synthesis, its manipulation
also became an essential task in 3D vision. However, existing manipulation
methods require extensive human labor, such as a user-provided semantic mask
and manual attribute search unsuitable for non-expert users. Instead, our
approach is designed to require a single text to manipulate a face
reconstructed with NeRF. To do so, we first train a scene manipulator, a latent
code-conditional deformable NeRF, over a dynamic scene to control a face
deformation using the latent code. However, representing a scene deformation
with a single latent code is unfavorable for compositing local deformations
observed in different instances. As so, our proposed Position-conditional
Anchor Compositor (PAC) learns to represent a manipulated scene with spatially
varying latent codes. Their renderings with the scene manipulator are then
optimized to yield high cosine similarity to a target text in CLIP embedding
space for text-driven manipulation. To the best of our knowledge, our approach
is the first to address the text-driven manipulation of a face reconstructed
with NeRF. Extensive results, comparisons, and ablation studies demonstrate the
effectiveness of our approach.
The proposed Position-conditional Anchor Compositor (PAC) enables text-driven manipulation of NeRF-reconstructed faces using spatially varying latent codes and CLIP embedding optimization, requiring minimal human input.
Neural Radiance Fields NeRF 3D face reconstruction novel view synthesis scene manipulator latent code-conditional deformable NeRF Position-conditional Anchor Compositor PAC scene deformation spatially varying latent codes CLIP embedding space text-driven manipulation