42 research outputs found
HeadOn: Real-time Reenactment of Human Portrait Videos
We propose HeadOn, the first real-time source-to-target reenactment approach
for complete human portrait videos that enables transfer of torso and head
motion, face expression, and eye gaze. Given a short RGB-D video of the target
actor, we automatically construct a personalized geometry proxy that embeds a
parametric head, eye, and kinematic torso model. A novel real-time reenactment
algorithm employs this proxy to photo-realistically map the captured motion
from the source actor to the target actor. On top of the coarse geometric
proxy, we propose a video-based rendering technique that composites the
modified target portrait video via view- and pose-dependent texturing, and
creates photo-realistic imagery of the target actor under novel torso and head
poses, facial expressions, and gaze directions. To this end, we propose a
robust tracking of the face and torso of the source actor. We extensively
evaluate our approach and show significant improvements in enabling much
greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at
Siggraph'1
Instant Volumetric Head Avatars
We present Instant Volumetric Head Avatars (INSTA), a novel approach for
reconstructing photo-realistic digital avatars instantaneously. INSTA models a
dynamic neural radiance field based on neural graphics primitives embedded
around a parametric face model. Our pipeline is trained on a single monocular
RGB portrait video that observes the subject under different expressions and
views. While state-of-the-art methods take up to several days to train an
avatar, our method can reconstruct a digital avatar in less than 10 minutes on
modern GPU hardware, which is orders of magnitude faster than previous
solutions. In addition, it allows for the interactive rendering of novel poses
and expressions. By leveraging the geometry prior of the underlying parametric
face model, we demonstrate that INSTA extrapolates to unseen poses. In
quantitative and qualitative studies on various subjects, INSTA outperforms
state-of-the-art methods regarding rendering quality and training time.Comment: Website: https://zielon.github.io/insta/ Video:
https://youtu.be/HOgaeWTih7
DeepVoxels: Learning Persistent 3D Feature Embeddings
In this work, we address the lack of 3D understanding of generative neural
networks by introducing a persistent 3D feature embedding for view synthesis.
To this end, we propose DeepVoxels, a learned representation that encodes the
view-dependent appearance of a 3D scene without having to explicitly model its
geometry. At its core, our approach is based on a Cartesian 3D grid of
persistent embedded features that learn to make use of the underlying 3D scene
structure. Our approach combines insights from 3D geometric computer vision
with recent advances in learning image-to-image mappings based on adversarial
loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction
of the scene, using a 2D re-rendering loss and enforces perspective and
multi-view geometry in a principled manner. We apply our persistent 3D scene
representation to the problem of novel view synthesis demonstrating
high-quality results for a variety of challenging scenes.Comment: Video: https://www.youtube.com/watch?v=HM_WsZhoGXw Supplemental
material:
https://drive.google.com/file/d/1BnZRyNcVUty6-LxAstN83H79ktUq8Cjp/view?usp=sharing
Code: https://github.com/vsitzmann/deepvoxels Project page:
https://vsitzmann.github.io/deepvoxels
DINER: Depth-aware Image-based NEural Radiance fields
We present Depth-aware Image-based NEural Radiance fields (DINER). Given a
sparse set of RGB input views, we predict depth and feature maps to guide the
reconstruction of a volumetric scene representation that allows us to render 3D
objects under novel views. Specifically, we propose novel techniques to
incorporate depth information into feature fusion and efficient scene sampling.
In comparison to the previous state of the art, DINER achieves higher synthesis
quality and can process input views with greater disparity. This allows us to
capture scenes more completely without changing capturing hardware requirements
and ultimately enables larger viewpoint changes during novel view synthesis. We
evaluate our method by synthesizing novel views, both for human heads and for
general objects, and observe significantly improved qualitative results and
increased perceptual metrics compared to the previous state of the art. The
code is publicly available for research purposes.Comment: Website: https://malteprinzler.github.io/projects/diner/diner.html ;
Video: https://www.youtube.com/watch?v=iI_fpjY5k8Y&t=1