AccelAes: Accelerating Diffusion Transformers for Training-Free Aesthetic-Enhanced Image Generation

Diffusion Transformers (DiTs) are a dominant backbone for high-fidelity text-to-image generation due to strong scalability and alignment at high resolutions. However, quadratic self-attention over dense spatial tokens leads to high inference latency and limits deployment. We observe that denoising is spatially non-uniform with respect to aesthetic descriptors in the prompt. Regions associated with aesthetic tokens receive concentrated cross-attention and show larger temporal variation, while low-affinity regions evolve smoothly with redundant computation. Based on this insight, we propose AccelAes, a training-free framework that accelerates DiTs through aesthetics-aware spatio-temporal reduction while improving perceptual aesthetics. AccelAes builds AesMask, a one-shot aesthetic focus mask derived from prompt semantics and cross-attention signals. When localized computation is feasible, SkipSparse reallocates computation and guidance to masked regions. We further reduce temporal redundancy using a lightweight step-level prediction cache that periodically replaces full Transformer evaluations. Experiments on representative DiT families show consistent acceleration and improved aesthetics-oriented quality. On Lumina-Next, AccelAes achieves a 2.11$\times$ speedup and improves ImageReward by +11.9% over the dense baseline. Code is available at https://github.com/xuanhuayin/AccelAes.

A Survey of 3D Reconstruction with Event Cameras: From Event-based Geometry to Neural 3D Rendering

Event cameras have emerged as promising sensors for 3D reconstruction due to their ability to capture per-pixel brightness changes asynchronously. Unlike conventional frame-based cameras, they produce sparse and temporally rich data streams, which enable more accurate 3D reconstruction and open up the possibility of performing reconstruction in extreme environments such as high-speed motion, low light, or high dynamic range scenes. In this survey, we provide the first comprehensive review focused exclusively on 3D reconstruction using event cameras. The survey categorises existing works into three major types based on input modality - stereo, monocular, and multimodal systems, and further classifies them by reconstruction approach, including geometry-based, deep learning-based, and recent neural rendering techniques such as Neural Radiance Fields and 3D Gaussian Splatting. Methods with a similar research focus were organised chronologically into the most subdivided groups. We also summarise public datasets relevant to event-based 3D reconstruction. Finally, we highlight current research limitations in data availability, evaluation, representation, and dynamic scene handling, and outline promising future research directions. This survey aims to serve as a comprehensive reference and a roadmap for future developments in event-driven 3D reconstruction.

A Survey on Event-driven 3D Reconstruction: Development under Different Categories

Event cameras have gained increasing attention for 3D reconstruction due to their high temporal resolution, low latency, and high dynamic range. They capture per-pixel brightness changes asynchronously, allowing accurate reconstruction under fast motion and challenging lighting conditions. In this survey, we provide a comprehensive review of event-driven 3D reconstruction methods, including stereo, monocular, and multimodal systems. We further categorize recent developments based on geometric, learning-based, and hybrid approaches. Emerging trends, such as neural radiance fields and 3D Gaussian splatting with event data, are also covered. The related works are structured chronologically to illustrate the innovations and progression within the field. To support future research, we also highlight key research gaps and future research directions in dataset, experiment, evaluation, event representation, etc.