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Found 6 papers, 6 papers with code
Retrieval-Augmented Score Distillation for Text-to-3D Generation
We leverage the retrieved asset to incorporate its geometric prior in the variational objective and adapt the diffusion model's 2D prior toward view consistency, achieving drastic improvements in both geometry and fidelity of generated scenes.
DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation
In the paradigm of AI-generated content (AIGC), there has been increasing attention to transferring knowledge from pre-trained text-to-image (T2I) models to text-to-video (T2V) generation.
Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation
In this work, we explore existing frameworks for score-distilling text-to-3D generation and identify the main causes of the view inconsistency problem -- the embedded bias of 2D diffusion models.
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic Models
Generative models have recently undergone significant advancement due to the diffusion models.
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters.
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity.
