由于模糊图像本身缺乏时间和纹理信息，因此非均匀的图像脱毛是一项具有挑战性的任务。来自辅助传感器的互补信息正在探索这些事件传感器以解决这些限制。后者可以异步记录对数强度的变化，称为事件，具有高时间分辨率和高动态范围。当前的基于事件的脱蓝晶方法将模糊图像与事件结合在一起，以共同估计每个像素运动和DeBlur操作员。在本文中，我们认为一种分裂和争议的方法更适合此任务。为此，我们建议使用调制可变形的卷积，其内核偏移和调制掩模是从事件中动态估算的，以编码场景中的运动，而从模糊图像和相应事件的组合中学习了deblur操作员。此外，我们采用了一种粗到十的多尺度重建方法来应对低对比度区域中事件的固有稀疏性。重要的是，我们介绍了第一个数据集，其中包含对曝光时间内的真实RGB模糊图像和相关事件的对。我们的结果在使用事件时显示出更好的总体鲁棒性，在合成数据上，PSNR的改进最多可提高1.57db，而对真实事件数据的改进则提高了1.08 dB。

Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects. This work presents DisCoScene: a 3Daware generative model for high-quality and controllable scene synthesis. The key ingredient of our method is a very abstract object-level representation (i.e., 3D bounding boxes without semantic annotation) as the scene layout prior, which is simple to obtain, general to describe various scene contents, and yet informative to disentangle objects and background. Moreover, it serves as an intuitive user control for scene editing. Based on such a prior, the proposed model spatially disentangles the whole scene into object-centric generative radiance fields by learning on only 2D images with the global-local discrimination. Our model obtains the generation fidelity and editing flexibility of individual objects while being able to efficiently compose objects and the background into a complete scene. We demonstrate state-of-the-art performance on many scene datasets, including the challenging Waymo outdoor dataset. Project page: https://snap-research.github.io/discoscene/

With the success of Vision Transformers (ViTs) in computer vision tasks, recent arts try to optimize the performance and complexity of ViTs to enable efficient deployment on mobile devices. Multiple approaches are proposed to accelerate attention mechanism, improve inefficient designs, or incorporate mobile-friendly lightweight convolutions to form hybrid architectures. However, ViT and its variants still have higher latency or considerably more parameters than lightweight CNNs, even true for the years-old MobileNet. In practice, latency and size are both crucial for efficient deployment on resource-constraint hardware. In this work, we investigate a central question, can transformer models run as fast as MobileNet and maintain a similar size? We revisit the design choices of ViTs and propose an improved supernet with low latency and high parameter efficiency. We further introduce a fine-grained joint search strategy that can find efficient architectures by optimizing latency and number of parameters simultaneously. The proposed models, EfficientFormerV2, achieve about $4\%$ higher top-1 accuracy than MobileNetV2 and MobileNetV2$\times1.4$ on ImageNet-1K with similar latency and parameters. We demonstrate that properly designed and optimized vision transformers can achieve high performance with MobileNet-level size and speed.

Recent efforts in Neural Rendering Fields (NeRF) have shown impressive results on novel view synthesis by utilizing implicit neural representation to represent 3D scenes. Due to the process of volumetric rendering, the inference speed for NeRF is extremely slow, limiting the application scenarios of utilizing NeRF on resource-constrained hardware, such as mobile devices. Many works have been conducted to reduce the latency of running NeRF models. However, most of them still require high-end GPU for acceleration or extra storage memory, which is all unavailable on mobile devices. Another emerging direction utilizes the neural light field (NeLF) for speedup, as only one forward pass is performed on a ray to predict the pixel color. Nevertheless, to reach a similar rendering quality as NeRF, the network in NeLF is designed with intensive computation, which is not mobile-friendly. In this work, we propose an efficient network that runs in real-time on mobile devices for neural rendering. We follow the setting of NeLF to train our network. Unlike existing works, we introduce a novel network architecture that runs efficiently on mobile devices with low latency and small size, i.e., saving $15\times \sim 24\times$ storage compared with MobileNeRF. Our model achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world scenes on mobile devices, e.g., $18.04$ms (iPhone 13) for rendering one $1008\times756$ image of real 3D scenes. Additionally, we achieve similar image quality as NeRF and better quality than MobileNeRF (PSNR $26.15$ vs. $25.91$ on the real-world forward-facing dataset).

Natural language interaction is a promising direction for democratizing 3D shape design. However, existing methods for text-driven 3D shape editing face challenges in producing decoupled, local edits to 3D shapes. We address this problem by learning disentangled latent representations that ground language in 3D geometry. To this end, we propose a complementary tool set including a novel network architecture, a disentanglement loss, and a new editing procedure. Additionally, to measure edit locality, we define a new metric that we call part-wise edit precision. We show that our method outperforms existing SOTA methods by 20% in terms of edit locality, and up to 6.6% in terms of language reference resolution accuracy. Our work suggests that by solely disentangling language representations, downstream 3D shape editing can become more local to relevant parts, even if the model was never given explicit part-based supervision.

In this work, we present a novel framework built to simplify 3D asset generation for amateur users. To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including images, text, partially observed shapes and combinations of these, further allowing to adjust the strength of each input. At the core of our approach is an encoder-decoder, compressing 3D shapes into a compact latent representation, upon which a diffusion model is learned. To enable a variety of multi-modal inputs, we employ task-specific encoders with dropout followed by a cross-attention mechanism. Due to its flexibility, our model naturally supports a variety of tasks, outperforming prior works on shape completion, image-based 3D reconstruction, and text-to-3D. Most interestingly, our model can combine all these tasks into one swiss-army-knife tool, enabling the user to perform shape generation using incomplete shapes, images, and textual descriptions at the same time, providing the relative weights for each input and facilitating interactivity. Despite our approach being shape-only, we further show an efficient method to texture the generated shape using large-scale text-to-image models.

There has been a recent explosion of impressive generative models that can produce high quality images (or videos) conditioned on text descriptions. However, all such approaches rely on conditional sentences that contain unambiguous descriptions of scenes and main actors in them. Therefore employing such models for more complex task of story visualization, where naturally references and co-references exist, and one requires to reason about when to maintain consistency of actors and backgrounds across frames/scenes, and when not to, based on story progression, remains a challenge. In this work, we address the aforementioned challenges and propose a novel autoregressive diffusion-based framework with a visual memory module that implicitly captures the actor and background context across the generated frames. Sentence-conditioned soft attention over the memories enables effective reference resolution and learns to maintain scene and actor consistency when needed. To validate the effectiveness of our approach, we extend the MUGEN dataset and introduce additional characters, backgrounds and referencing in multi-sentence storylines. Our experiments for story generation on the MUGEN, the PororoSV and the FlintstonesSV dataset show that our method not only outperforms prior state-of-the-art in generating frames with high visual quality, which are consistent with the story, but also models appropriate correspondences between the characters and the background.

最近，稀疏培训已成为有希望的范式，可在边缘设备上有效地深入学习。当前的研究主要致力于通过进一步增加模型稀疏性来降低培训成本。但是，增加的稀疏性并不总是理想的，因为它不可避免地会在极高的稀疏度下引入严重的准确性降解。本文打算探索其他可能的方向，以有效，有效地降低稀疏培训成本，同时保持准确性。为此，我们研究了两种技术，即层冻结和数据筛分。首先，层冻结方法在密集的模型训练和微调方面取得了成功，但在稀疏训练域中从未采用过。然而，稀疏训练的独特特征可能会阻碍层冻结技术的结合。因此，我们分析了在稀疏培训中使用层冻结技术的可行性和潜力，并发现它有可能节省大量培训成本。其次，我们提出了一种用于数据集有效培训的数据筛分方法，该方法通过确保在整个培训过程中仅使用部分数据集来进一步降低培训成本。我们表明，这两种技术都可以很好地整合到稀疏训练算法中，以形成一个通用框架，我们将其配置为SPFDE。我们的广泛实验表明，SPFDE可以显着降低培训成本，同时从三个维度中保留准确性：重量稀疏性，层冻结和数据集筛分。

在机器人研究中，在不平坦的地形中安全导航是一个重要的问题。在本文中，我们提出了一个2.5D导航系统，该系统包括高程图构建，路径规划和本地路径，随后避免了障碍。对于本地路径，我们使用模型预测路径积分（MPPI）控制方法。我们为MPPI提出了新的成本功能，以使其适应高程图和通过不平衡运动。我们在多个合成测试和具有不同类型的障碍物和粗糙表面的模拟环境中评估系统。

创建和编辑3D对象的形状和颜色需要巨大的人类努力和专业知识。与3D接口中的直​​接操作相比，诸如草图和涂鸦之类的2D交互对用户通常更自然和直观。在本文中，我们提出了一个通用的多模式生成模型，该模型通过共享的潜在空间耦合2D模式和隐式3D表示。通过提出的模型，通过简单地通过潜在空间从特定的2D控制模式传播编辑，可以实现多功能3D生成和操纵。例如，通过绘制草图来编辑3D形状，通过绘画颜色在2D渲染上重新色彩，或者在一个或几个参考图像中生成特定类别的3D形状。与先前的作品不同，我们的模型不需要每个编辑任务进行重新训练或微调，并且在概念上也很简单，易于实现，对输入域移动的强大，并且可以在部分2D输入中进行多样化的重建。我们在灰度线草图和渲染颜色图像的两种代表性2D模态上评估了我们的框架，并证明我们的方法可以通过以下2D模态实现各种形状的操纵和生成任务。