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Abstract. We present 4Diff, a 3D-aware diffusion model addressing the exo-to-ego viewpoint translation task—generating first-person (egocentric) view images from the corresponding third-person (exocentric) images. Building on the diffusion model’s ability to generate photorealistic images, we propose a transformer-based diffusion model that incorporates geometry priors through two mechanisms: (i) egocentric point cloud rasterization and (ii) 3D-aware rotary cross-attention. Egocentric point cloud rasterization converts the input exocentric image into an egocentric layout, which is subsequently used by a diffusion image transformer. As a component of the diffusion transformer’s denoiser block, the 3D-aware rotary cross-attention further incorporates 3D information and semantic features from the source exocentric view. Our 4Diff achieves state-of-the-art results on the challenging and diverse Ego-Exo4D multiview dataset and exhibits robust generalization to novel environments not encountered during training. Our code, processed data, and pretrained models are publicly available at https://klauscc.github.io/4diff. 

Gradient-based approximate inference methods, such as Stein variational gradient descent (SVGD), provide simple and general-purpose inference engines for differentiable continuous distributions. However, existing forms of SVGD cannot be directly applied to discrete distributions. In this work, we fill this gap by proposing a simple yet general framework that transforms discrete distributions to equivalent piecewise continuous distributions, on which the gradient-free SVGD is applied to perform efficient approximate inference. The empirical results show that our method outperforms traditional algorithms such as Gibbs sampling and discontinuous Hamiltonian Monte Carlo on various challenging benchmarks of discrete graphical models. We demonstrate that our method provides a promising tool for learning ensembles of binarized neural network (BNN), outperforming other widely used ensemble methods on learning binarized AlexNet on CIFAR-10 dataset. In addition, such transform can be straightforwardly employed in gradient-free kernelized Stein discrepancy to perform goodness-of-fit (GOF) test on discrete distributions. Our proposed method outperforms existing GOF test methods for intractable discrete distributions.