CCA: Collaborative Competitive Agents for Image Editing

This paper presents a novel generative model, Collaborative Competitive Agents (CCA), which leverages the capabilities of multiple Large Language Models (LLMs) based agents to execute complex tasks. Drawing inspiration from Generative Adversarial Networks (GANs), the CCA system employs two equal-status generator agents and a discriminator agent. The generators independently process user instructions and generate results, while the discriminator evaluates the outputs, and provides feedback for the generator agents to further reflect and improve the generation results. Unlike the previous generative model, our system can obtain the intermediate steps of generation. This allows each generator agent to learn from other successful executions due to its transparency, enabling a collaborative competition that enhances the quality and robustness of the system's results. The primary focus of this study is image editing, demonstrating the CCA's ability to handle intricate instructions robustly. The paper's main contributions include the introduction of a multi-agent-based generative model with controllable intermediate steps and iterative optimization, a detailed examination of agent relationships, and comprehensive experiments on image editing. Code is available at \href{https://github.com/TiankaiHang/CCA}{https://github.com/TiankaiHang/CCA}

Real-time smoke rendering using compensated ray marching

We present a real-time algorithm called compensated ray march-ing for rendering of smoke under dynamic low-frequency environ-ment lighting. Our approach is based on a decomposition of the input smoke animation, represented as a sequence of volumetric density fields, into a set of radial basis functions (RBFs) and a se-quence of residual fields. To expedite rendering, the source radi-ance distribution within the smoke is computed from only the low-frequency RBF approximation of the density fields, since the high-frequency residuals have little impact on global illumination under low-frequency environment lighting. Furthermore, in computing source radiances the contributions from single and multiple scatter-ing are evaluated at only the RBF centers and then approximated at other points in the volume using an RBF-based interpolation. A slice-based integration of these source radiances along each view ray is then performed to render the final image. The high-frequency residual fields, which are a critical component in the local appear-ance of smoke, are compensated back into the radiance integral dur-ing this ray march to generate images of high detail. The runtime algorithm, which includes both light transfer simula-tion and ray marching, can be easily implemented on the GPU, and thus allows for real-time manipulation of viewpoint and lighting, as well as interactive editing of smoke attributes such as extinction cross section, scattering albedo, and phase function. Only moderate preprocessing time and storage is needed. This approach provides the first method for real-time smoke rendering that includes sin-gle and multiple scattering while generating results comparable in quality to offline algorithms like ray tracing

Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

Pretrained backbones with fine-tuning have been widely adopted in 2D vision and natural language processing tasks and demonstrated significant advantages to task-specific networks. In this paper, we present a pretrained 3D backbone, named {\SST}, which first outperforms all state-of-the-art methods in downstream 3D indoor scene understanding tasks. Our backbone network is based on a 3D Swin transformer and carefully designed to efficiently conduct self-attention on sparse voxels with linear memory complexity and capture the irregularity of point signals via generalized contextual relative positional embedding. Based on this backbone design, we pretrained a large {\SST} model on a synthetic Structed3D dataset that is 10 times larger than the ScanNet dataset and fine-tuned the pretrained model in various downstream real-world indoor scene understanding tasks. The results demonstrate that our model pretrained on the synthetic dataset not only exhibits good generality in both downstream segmentation and detection on real 3D point datasets, but also surpasses the state-of-the-art methods on downstream tasks after fine-tuning with +2.3 mIoU and +2.2 mIoU on S3DIS Area5 and 6-fold semantic segmentation, +2.1 mIoU on ScanNet segmentation (val), +1.9 [email protected] on ScanNet detection, +8.1 [email protected] on S3DIS detection. Our method demonstrates the great potential of pretrained 3D backbones with fine-tuning for 3D understanding tasks. The code and models are available at https://github.com/microsoft/Swin3D