Search Results for author:
Found 10 papers, 4 papers with code
GS-ID: Illumination Decomposition on Gaussian Splatting via Diffusion Prior and Parametric Light Source Optimization
We present GS-ID, a novel framework for illumination decomposition on Gaussian Splatting, achieving photorealistic novel view synthesis and intuitive light editing.
Causal Inference from Slowly Varying Nonstationary Processes
Causal inference from observational data following the restricted structural causal models (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or non-linearity.
Mining Invariance from Nonlinear Multi-Environment Data: Binary Classification
Making predictions in an unseen environment given data from multiple training environments is a challenging task.
Low-Rank Approximation of Structural Redundancy for Self-Supervised Learning
We study the data-generating mechanism for reconstructive SSL to shed light on its effectiveness.
PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models
Personalized text-to-image generation has emerged as a powerful and sought-after tool, empowering users to create customized images based on their specific concepts and prompts.
AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose
We present AvatarVerse, a stable pipeline for generating expressive high-quality 3D avatars from nothing but text descriptions and pose guidance.
Generalized Invariant Matching Property via LASSO
In this work, by formulating a high-dimensional problem with intrinsic sparsity, we generalize the invariant matching property for an important setting when only the target is intervened.
Learning Invariant Representations under General Interventions on the Response
One principled approach is to adopt the structural causal models to describe training and test models, following the invariance principle which says that the conditional distribution of the response given its predictors remains the same across environments.
An Invariant Matching Property for Distribution Generalization under Intervened Response
The task of distribution generalization concerns making reliable prediction of a response in unseen environments.
Causal Inference from Slowly Varying Nonstationary Processes
Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or nonlinearity.
