Recent CLIP-guided 3D optimization methods, e.g., DreamFields and PureCLIPNeRF achieve great success in zero-shot text-guided 3D synthesis. However, due to the scratch training and random initialization without any prior knowledge, these methods usually fail to generate accurate and faithful 3D structures that conform to the corresponding text. In this paper, we make the first attempt to introduce the explicit 3D shape prior to CLIP-guided 3D optimization methods. Specifically, we first generate a high-quality 3D shape from input texts in the text-to-shape stage as the 3D shape prior. We then utilize it as the initialization of a neural radiance field and then optimize it with the full prompt. For the text-to-shape generation, we present a simple yet effective approach that directly bridges the text and image modalities with a powerful text-to-image diffusion model. To narrow the style domain gap between images synthesized by the text-to-image model and shape renderings used to train the image-to-shape generator, we further propose to jointly optimize a learnable text prompt and fine-tune the text-to-image diffusion model for rendering-style image generation. Our method, namely, Dream3D, is capable of generating imaginative 3D content with better visual quality and shape accuracy than state-of-the-art methods.

We represent the ResNeRF, a novel geometry-guided two-stage framework for indoor scene novel view synthesis. Be aware of that a good geometry would greatly boost the performance of novel view synthesis, and to avoid the geometry ambiguity issue, we propose to characterize the density distribution of the scene based on a base density estimated from scene geometry and a residual density parameterized by the geometry. In the first stage, we focus on geometry reconstruction based on SDF representation, which would lead to a good geometry surface of the scene and also a sharp density. In the second stage, the residual density is learned based on the SDF learned in the first stage for encoding more details about the appearance. In this way, our method can better learn the density distribution with the geometry prior for high-fidelity novel view synthesis while preserving the 3D structures. Experiments on large-scale indoor scenes with many less-observed and textureless areas show that with the good 3D surface, our method achieves state-of-the-art performance for novel view synthesis.

我们提出了联合隐式功能（UNIF），这是一种基于原始扫描和骨骼作为输入的人类重建和动画的零件方法。先前的基于部分的人重建方法依赖于SMPL的地面零件标签，因此仅限于最小衣服。相比之下，我们的方法学会了将部分与身体运动分开，而不是部分监督，因此可以扩展到穿衣服的人类和其他铰接的物体。我们的分区从动作进行分区是通过以骨骼为中心的初始化，骨限度损失和正常损失来实现的，即使训练姿势受到限制，也可以确保稳定的零件分裂。我们还为SDF提供了最小的周边损失，以抑制额外的表面和部分重叠。我们方法的另一个核心是一种相邻的部分接缝算法，该算法会产生非刚性变形，以维持显着缓解基于部分伪像的部分之间的连接。在该算法下，我们进一步提出了“竞争部分”，该方法通过点对骨骼而不是绝对位置的相对位置定义了重量，从而避免了神经隐式函数的概括性问题（线性混合皮肤）。我们通过在CAPE和ClothSeq数据集上穿衣服的人体重建和动画来证明我们方法的有效性。

车道检测是许多实际自治系统的重要组成部分。尽管已经提出了各种各样的车道检测方法，但随着时间的推移报告了基准的稳定改善，但车道检测仍然是一个未解决的问题。这是因为大多数现有的车道检测方法要么将车道检测视为密集的预测或检测任务，因此很少有人考虑泳道标记的独特拓扑（Y形，叉形，几乎是水平的车道），该拓扑标记物是该标记的。导致亚最佳溶液。在本文中，我们提出了一种基于继电器链预测的新方法检测。具体而言，我们的模型预测了分割图以对前景和背景区域进行分类。对于前景区域中的每个像素点，我们穿过前向分支和后向分支以恢复整个车道。每个分支都会解码传输图和距离图，以产生移动到下一个点的方向，以及逐步预测继电器站的步骤（下一个点）。因此，我们的模型能够沿车道捕获关键点。尽管它很简单，但我们的策略使我们能够在包括Tusimple，Culane，Curvelanes和Llamas在内的四个主要基准上建立新的最先进。

在本文中，我们提出了一种新的序列验证任务，该任务旨在区分从具有阶梯级变换的负面的正视频对，但仍然进行相同的任务。这种具有挑战性的任务驻留在没有先前操作检测或需要事件级别甚至帧级注释的分段的开放式设置。为此，我们仔细重新组成了具有步骤过程任务结构的两个公开的动作相关的数据集。为了充分调查任何方法的有效性，我们收集了统计化学实验中各种步进变换的脚本视频数据集。此外，引入了一种新的评估度量加权距离比以确保评估期间不同的步进级变换等效。最后，基于具有新序列对准损耗的变压器的简单但有效的基线被引入到更好地表征步骤之间的长期依赖性，这优于其他动作识别方法。将发布代码和数据。

变压器在许多视觉任务上表现出优选的性能。然而，对于人的任务重新识别（Reid），Vanilla变形金刚将丰富的背景留下了高阶特征关系，这是由于行人的戏剧性变化而不足的局部特征细节。在这项工作中，我们提出了一个全部关系高阶变压器（OH-Figrain）来模拟Reid的全系关系功能。首先，为了加强视觉表示的能力，而不是基于每个空间位置的对查询和隔离键获得注意矩阵，我们进一步逐步以模拟非本地机制的高阶统计信息。我们以先前的混合机制在每个订单的相应层中共享注意力，以降低计算成本。然后，提出了一种基于卷积的本地关系感知模块来提取本地关系和2D位置信息。我们模型的实验结果是优越的有前途，其在市场上显示出最先进的性能-1501，Dukemtmc，MSMT17和occluded-Duke数据集。

Three-dimensional (3D) ultrasound imaging technique has been applied for scoliosis assessment, but current assessment method only uses coronal projection image and cannot illustrate the 3D deformity and vertebra rotation. The vertebra detection is essential to reveal 3D spine information, but the detection task is challenging due to complex data and limited annotations. We propose VertMatch, a two-step framework to detect vertebral structures in 3D ultrasound volume by utilizing unlabeled data in semi-supervised manner. The first step is to detect the possible positions of structures on transverse slice globally, and then the local patches are cropped based on detected positions. The second step is to distinguish whether the patches contain real vertebral structures and screen the predicted positions from the first step. VertMatch develops three novel components for semi-supervised learning: for position detection in the first step, (1) anatomical prior is used to screen pseudo labels generated from confidence threshold method; (2) multi-slice consistency is used to utilize more unlabeled data by inputting multiple adjacent slices; (3) for patch identification in the second step, the categories are rebalanced in each batch to solve imbalance problem. Experimental results demonstrate that VertMatch can detect vertebra accurately in ultrasound volume and outperforms state-of-the-art methods. VertMatch is also validated in clinical application on forty ultrasound scans, and it can be a promising approach for 3D assessment of scoliosis.

Lifelong person re-identification (LReID) is in significant demand for real-world development as a large amount of ReID data is captured from diverse locations over time and cannot be accessed at once inherently. However, a key challenge for LReID is how to incrementally preserve old knowledge and gradually add new capabilities to the system. Unlike most existing LReID methods, which mainly focus on dealing with catastrophic forgetting, our focus is on a more challenging problem, which is, not only trying to reduce the forgetting on old tasks but also aiming to improve the model performance on both new and old tasks during the lifelong learning process. Inspired by the biological process of human cognition where the somatosensory neocortex and the hippocampus work together in memory consolidation, we formulated a model called Knowledge Refreshing and Consolidation (KRC) that achieves both positive forward and backward transfer. More specifically, a knowledge refreshing scheme is incorporated with the knowledge rehearsal mechanism to enable bi-directional knowledge transfer by introducing a dynamic memory model and an adaptive working model. Moreover, a knowledge consolidation scheme operating on the dual space further improves model stability over the long term. Extensive evaluations show KRC's superiority over the state-of-the-art LReID methods on challenging pedestrian benchmarks.

在规范空间中对人体进行建模是捕捉和动画的常见实践。但是，当涉及神经辐射场（NERF）时，在规范空间中学习静态NERF是不够的，因为即使人体移动时，即使场景照明是恒定的，身体的照明也会变化。以前的方法通过学习人均嵌入来减轻照明的不一致，但是此操作并不能推广到看不见的姿势。鉴于照明条件在世界空间中是静态的，而人体在规范空间中是一致的，我们提出了一个双空间的nerf，该nerf在场景照明和人体中对两个单独空间的两个MLP进行建模。为了弥合这两个空间，以前的方法主要依赖于线性混合剥皮（LBS）算法。但是，动态神经场的LB的混合重量很难棘手，因此通常用另一个MLP记住，这不会推广到新型姿势。尽管可以借用参数网格（例如SMPL）的混合权重，但插值操作会引入更多的伪像。在本文中，我们建议使用Barycentric映射，该映射可以直接概括为看不见的姿势并出奇地取得了比具有神经混合重量的LB的优势。人类36M和ZJU-MOCAP数据集的定量和定性结果显示了我们方法的有效性。

通过恢复（实体瘤的响应评估标准）自动测量病变/肿瘤大小，直径和分割对于计算机辅助诊断很重要。尽管近年来已经研究了它，但仍有空间可以提高其准确性和鲁棒性，例如（1）通过合并丰富的上下文信息来增强功能，同时保持高空间分辨率，（2）涉及新任务和损失以进行关节优化。为了实现这一目标，本文提出了一个基于变压器的网络（Meaformer，测量变压器），用于病变恢复直径预测和分割（LRDPS）。它被配制为三个相关和互补任务：病变分割，热图预测和关键点回归。据我们所知，这是首次使用按键重点回归进行恢复直径预测。 MeaeFormer可以通过使用变压器来捕获其远程依赖性来增强高分辨率功能。引入了两个一致性损失，以明确建立这些任务之间的关系，以更好地优化。实验表明，MeAformer实现了LRDP在大规模深层数据集上的最新性能，并在纵向研究中产生了两个下游诊所的任务，即3D病变细分和恢复评估。