Back to Basics: Motion Representation Matters for Human Motion Generation Using Diffusion Model

A new method called Soft Q-based Diffusion Finetuning (SQDF) has been proposed to enhance the alignment of diffusion models with downstream objectives, addressing issues of reward over-optimization that lead to unnatural samples. This method incorporates a reparameterized policy gradient of a differentiable soft Q-function estimation, along with innovations like a discount factor for credit assignment and off-policy replay buffers.

A new framework called Measurement-Aware Consistency Sampling (MACS) has been introduced to enhance the efficiency of diffusion models in solving inverse imaging problems. This approach utilizes a measurement-consistency mechanism to regulate stochasticity, ensuring fidelity to observed data while maintaining computational efficiency. Comprehensive experiments on datasets like Fashion-MNIST and LSUN Bedroom show significant improvements in both perceptual and pixel-level quality.

GalaxyDiT has been introduced as a training-free method to enhance the efficiency of video generation using diffusion transformers, addressing the computational inefficiencies associated with existing models that require extensive iterative steps and resources. This innovation focuses on guidance alignment and adaptive proxy selection to optimize computational reuse across different model families.

A new framework has been introduced that reinterprets Maximum Entropy Reinforcement Learning (MaxEntRL) as a diffusion model-based sampling problem, focusing on minimizing the reverse Kullback-Leibler divergence between the diffusion policy and the optimal policy distribution. This leads to the development of diffusion-based variants of existing algorithms like Soft Actor-Critic, Proximal Policy Optimization, and Wasserstein Policy Optimization, termed DiffSAC, DiffPPO, and DiffWPO.

Recent advancements in diffusion models have led to the development of a phase-aware strategy that accelerates image generation by applying different speedups to various stages of the process. This approach utilizes lightweight LoRA adapters, named Slow-LoRA and Fast-LoRA, to enhance efficiency without extensive retraining of models.