RoDynRF: Robust Dynamic Radiance Fields Paid Members Public
In this work, the authors address the robustness issue of dynamic radiance field reconstruction methods by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length). Learn how they do it!
AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging Paid Members Public
AgileAvatar is a novel self-supervised learning framework to create high-quality stylized 3D avatars with a mix of continuous and discrete parameters. To ensure the discrete parameters are optimized, a cascaded relaxation-and-search pipeline is implemented.
Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution Paid Members Public
Box2Mask is a novel single-shot instance segmentation approach, which integrates the classical level-set evolution model into deep neural network learning to achieve accurate mask prediction with only bounding box supervision. Check the paper out!
Zero-Shot Text-Guided Object Generation with Dream Fields Paid Members Public
Dream Fields can generate the geometry and color of a wide range of objects without 3D supervision. It combines neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions. Take a look!
InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds Paid Members Public
InstantAvatar is a system that can reconstruct human avatars from a monocular video within seconds, and these avatars can be animated and rendered at an interactive rate. It converges 130x faster and can be trained in minutes instead of hours, way faster than competitors.
Scalable Diffusion Models with Transformers Paid Members Public
In this work, the researchers explore a new class of diffusion models based on the transformer architecture; train latent diffusion models, replacing the U-Net backbone with a transformer that operates on latent patches; and analyze the scalability of Diffusion Transformers (DiTs).
NeRF-Art: Text-Driven Neural Radiance Fields Stylization Paid Members Public
Neural radiance fields (NeRF) enable high-quality novel view synthesis. Editing NeRF, however, remains challenging. In this paper, the authors present NeRF-Art, a text-guided NeRF stylization approach that manipulates the style of a pre-trained NeRF model with a single text prompt.
ECON: Explicit Clothed humans Obtained from Normals Paid Members Public
ECON combines the best aspects of implicit and explicit surfaces to infer high-fidelity 3D humans, even with loose clothing or in challenging poses. ECON is more accurate than the state of the art. Perceptual studies also show that ECON’s perceived realism is better by a large margin.