694 research outputs found
Twisted-mass reweighting for O(a) improved Wilson fermions
We test the reweighting of the quark determinant of O(a) improved Wilson
fermions in the domain-decomposed hybrid Monte-Carlo algorithm. Specifically,
we implement a reweighting in a twisted-mass parameter proposed by Palombi and
L\"uscher in QCD. We find that at equal acceptance rate, the
algorithm is significantly more stable on a lattice upon
switching on the reweighting parameter. At the same time, the reweighting
factor does not fluctuate strongly and hence is under control. At equal
statistics, the uncertainty on the pion correlator is comparable to the case of
the standard, unreweighted algorithm.Comment: 7 pages, 5 figures, XXIX International Symposium On Lattice Field
Theor
BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis
Synthesizing photorealistic 4D human head avatars from videos is essential
for VR/AR, telepresence, and video game applications. Although existing Neural
Radiance Fields (NeRF)-based methods achieve high-fidelity results, the
computational expense limits their use in real-time applications. To overcome
this limitation, we introduce BakedAvatar, a novel representation for real-time
neural head avatar synthesis, deployable in a standard polygon rasterization
pipeline. Our approach extracts deformable multi-layer meshes from learned
isosurfaces of the head and computes expression-, pose-, and view-dependent
appearances that can be baked into static textures for efficient rasterization.
We thus propose a three-stage pipeline for neural head avatar synthesis, which
includes learning continuous deformation, manifold, and radiance fields,
extracting layered meshes and textures, and fine-tuning texture details with
differential rasterization. Experimental results demonstrate that our
representation generates synthesis results of comparable quality to other
state-of-the-art methods while significantly reducing the inference time
required. We further showcase various head avatar synthesis results from
monocular videos, including view synthesis, face reenactment, expression
editing, and pose editing, all at interactive frame rates.Comment: ACM Transactions on Graphics (SIGGRAPH Asia 2023). Project Page:
https://buaavrcg.github.io/BakedAvata
Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding
Open-vocabulary querying in 3D space is challenging but essential for scene
understanding tasks such as object localization and segmentation.
Language-embedded scene representations have made progress by incorporating
language features into 3D spaces. However, their efficacy heavily depends on
neural networks that are resource-intensive in training and rendering. Although
recent 3D Gaussians offer efficient and high-quality novel view synthesis,
directly embedding language features in them leads to prohibitive memory usage
and decreased performance. In this work, we introduce Language Embedded 3D
Gaussians, a novel scene representation for open-vocabulary query tasks.
Instead of embedding high-dimensional raw semantic features on 3D Gaussians, we
propose a dedicated quantization scheme that drastically alleviates the memory
requirement, and a novel embedding procedure that achieves smoother yet high
accuracy query, countering the multi-view feature inconsistencies and the
high-frequency inductive bias in point-based representations. Our comprehensive
experiments show that our representation achieves the best visual quality and
language querying accuracy across current language-embedded representations,
while maintaining real-time rendering frame rates on a single desktop GPU
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