A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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SSDNeRF unifies 3D generation and reconstruction by conjoining a triplane NeRF auto-decoder with a single-stage diffusion model.
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Tune-A-Video enables one-shot text-to-video generation by tuning a pretrained image diffusion model on a single video.
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This paper introduces flow matching and diffusion models, explaining and deriving flow matching as a scalable algorithm underlying large-scale generative models.
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Q-dit introduces a post-training quantization method for diffusion transformers that reduces memory and latency while maintaining generation quality.
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Lazydit introduces a lazy learning approach to accelerate Diffusion Transformers by selectively skipping redundant computations during inference.
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The paper argues that semantically richer latent representations can substantially improve diffusion Transformer optimization efficiency and expand the reconstruction–generation Pareto frontier.
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The paper shows that vision foundation models can serve as effective tokenizers for latent diffusion models, but distillation-based tokenizer training weakens performance.
Cheng Chi, Zhenjia Xu, Siyuan Feng, et al.
Diffusion Policy represents robot visuomotor policies as conditional denoising diffusion processes, outperforming state-of-the-art methods by 46.9% across 15 tasks.
Wenrui Bao, Zhiben Chen, Dan Xu, et al.
Learn2PD trains a lightweight filter to adaptively unmask tokens in diffusion LLMs, achieving up to 22.58x speedup without performance loss.
Yan Deng, Fei Xu
DreamFly introduces causal memory and receding-horizon diffusion planning to improve aerial vision-language navigation, achieving state-of-the-art results on the OpenFly benchmark.
Haodong Li, Shaoteng Liu, Tianyu Wang, et al.
Introduces Latent Dynamics Reasoning (LDR), a video world model that explicitly models latent dynamics via kinematic integration, achieving far better out-of-distribution extrapolation than video diffusion baselines.
He-Yen Hsieh, H. T. Kung
ReRound is a post-training quantization method that uses a conditional diffusion model to resolve midpoint ambiguity in round-to-nearest, improving low-bit LLM quantization without calibration data.