Europe
MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views Y uedong Chen
Diffusion (SVD) model, where these features then act as pose and visual cues to guide the denoising process and produce photorealistic 3D-consistent views. Our model is end-to-end trainable and supports rendering arbitrary views with as few as 5 sparse input views. To evaluate MVSplat360's performance, we introduce a new benchmark using the challenging DL3DV -10K dataset, where
L-CAD: Language-based Colorization with Any-level Descriptions using Diffusion Priors
With the proposed novel sampling strategy, our model achieves instance-aware colorization in diverse and complex scenarios.
Appendix A Datasheet for Datasets
The negative prompt used is " unrealistic, bad anatomy, wrong anatomy, extra limb, missing limb,
Learning from Uncertain Data: From Possible Worlds to Possible Models
Exploring every possibility and training a model for each is impractical.
Adaptive Principal Component Regression with Applications to Panel Data
Our framework may be thought of as a generalization of the synthetic control and synthetic interventions frameworks, where data is collected via an adaptive intervention assignment policy.
Adaptive Principal Component Regression with Applications to Panel Data
Our framework may be thought of as a generalization of the synthetic control and synthetic interventions frameworks, where data is collected via an adaptive intervention assignment policy.
One-step differentiation of iterative algorithms
For iterative algorithms, implicit differentiation alleviates this issue but requires custom implementation of Jacobian evaluation. In this paper, we study one-step differentiation, also known as Jacobian-free backpropagation, a method as easy as automatic differentiation and as efficient as implicit differentiation for fast algorithms (e.g., superlinear