SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions

The remarkable capabilities of pretrained image diffusion models have been utilized not only for generating fixed-size images but also for creating panoramas. However, naive stitching of multiple images often results in visible seams. Recent techniques have attempted to address this issue by performing joint diffusions in multiple windows and averaging latent features in overlapping regions. However, these approaches, which focus on seamless montage generation, often yield incoherent outputs by blending different scenes within a single image. To overcome this limitation, we propose SyncDiffusion, a plug-and-play module that synchronizes multiple diffusions through gradient descent from a perceptual similarity loss. Specifically, we compute the gradient of the perceptual loss using the predicted denoised images at each denoising step, providing meaningful guidance for achieving coherent montages. Our experimental results demonstrate that our method produces significantly more coherent outputs compared to previous methods (66.35% vs. 33.65% in our user study) while still maintaining fidelity (as assessed by GIQA) and compatibility with the input prompt (as measured by CLIP score).Comment: Project page: https://syncdiffusion.github.i

Rydberg Quantum Wires for Maximum Independent Set Problems with Nonplanar and High-Degree Graphs

One prominent application of near-term quantum computing devices is to solve combinatorial optimization such as non-deterministic polynomial-time hard (NP-hard) problems. Here we present experiments with Rydberg atoms to solve one of the NP-hard problems, the maximum independent set (MIS) of graphs. We introduce the Rydberg quantum wire scheme with auxiliary atoms to engineer long-ranged networks of qubit atoms. Three-dimensional (3D) Rydberg-atom arrays are constructed, overcoming the intrinsic limitations of two-dimensional arrays. We demonstrate Kuratowski subgraphs and a six-degree graph, which are the essentials of non-planar and high-degree graphs. Their MIS solutions are obtained by realizing a programmable quantum simulator with the quantum-wired 3D arrays. Our construction provides a way to engineer many-body entanglement, taking a step toward quantum advantages in combinatorial optimization.Comment: 8 pages, 4 figure

Im2Hands: Learning Attentive Implicit Representation of Interacting Two-Hand Shapes

We present Implicit Two Hands (Im2Hands), the first neural implicit representation of two interacting hands. Unlike existing methods on two-hand reconstruction that rely on a parametric hand model and/or low-resolution meshes, Im2Hands can produce fine-grained geometry of two hands with high hand-to-hand and hand-to-image coherency. To handle the shape complexity and interaction context between two hands, Im2Hands models the occupancy volume of two hands - conditioned on an RGB image and coarse 3D keypoints - by two novel attention-based modules responsible for (1) initial occupancy estimation and (2) context-aware occupancy refinement, respectively. Im2Hands first learns per-hand neural articulated occupancy in the canonical space designed for each hand using query-image attention. It then refines the initial two-hand occupancy in the posed space to enhance the coherency between the two hand shapes using query-anchor attention. In addition, we introduce an optional keypoint refinement module to enable robust two-hand shape estimation from predicted hand keypoints in a single-image reconstruction scenario. We experimentally demonstrate the effectiveness of Im2Hands on two-hand reconstruction in comparison to related methods, where ours achieves state-of-the-art results. Our code is publicly available at https://github.com/jyunlee/Im2Hands.Comment: 6 figures, 14 pages, accepted to CVPR 2023, project page: https://jyunlee.github.io/projects/implicit-two-hands