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Found 6 papers, 4 papers with code
DreamReward: Text-to-3D Generation with Human Preference
Building upon the 3D reward model, we finally perform theoretical analysis and present the Reward3D Feedback Learning (DreamFL), a direct tuning algorithm to optimize the multi-view diffusion models with a redefined scorer.
Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation
Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process through a single image or natural language.
Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior
Instead of retraining a costly viewpoint-aware model, we study how to fully exploit easily accessible coarse 3D knowledge to enhance the prompts and guide 2D lifting optimization for refinement.
Discovering Dynamic Causal Space for DAG Structure Learning
Discovering causal structure from purely observational data (i. e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning.
Semantic Ray: Learning a Generalizable Semantic Field with Cross-Reprojection Attention
In this paper, we aim to learn a semantic radiance field from multiple scenes that is accurate, efficient and generalizable.
Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting
Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges.
