Reconstructing fine-grained geometry of clothed human from single-view image is a challenging task, particularly in accurately recovering complex shapes and generating clothes details. To address these limitations, we propose a novel approach named HumanPro, which estimates high-quality human normals via a generative model, and progressively deforms a parametric body into the final clothed human mesh guided by normals. First, we propose a geometry-aware latent diffusion model with a normal enhancer to estimate high-quality human normals from four views. Then, we propose a progressive mesh optimization consisting of shape-aware deformation alignment and global-to-patch detail refinement for human mesh reconstruction. The shape-aware deformation alignment applies image morphing to learn the shape-level gap of normals, addressing large-scale deformation of complex clothes. It can recover the overall silhouette of a clothed human, and serves as an initialization for the global-to-patch detail refinement. Our detail refinement combines global and patch-wise optimization strategies to iteratively produce the clothed human mesh by minimizing the pixel-level difference of normals. This way effectively recovers fine-grained details while avoiding local minima. Extensive experiments demonstrate that HumanPro can deal with various challenging scenarios and outperforms state-of-the-art methods.

We present HumanFlow, a unified flow-matching-based framework that enables high-fidelity and controllable full-body human image generation under diverse human-centric control conditions. Despite recent progress, controllable human image generation poses a fundamental challenge in balancing high visual fidelity with strict adherence to human-centric control conditions. HumanFlow formulates human image generation as a conditional flow-matching process with deterministic generation dynamics. To incorporate such human-centric control conditions into the pretrained model, we introduce a unified control framework with Control Encoder and Token-ControlNet. A Control Encoder maps diverse conditions into a unified latent representation that is spatially aligned with the image latent space. Token-ControlNet is a lightweight control network architecturally aligned with the FLUX double-stream design. To address accurate structural control over human bodies, we further propose the Human Topology Consistency Loss (HTCL). HTCL regularizes conditional flow matching by constraining generated human configurations to a union of statistically grounded topology manifolds defined by normalized bone ratios and joint angles. To support large-scale training and systematic evaluation, we construct MiCoGen, a multi-condition human image dataset comprising over one million full-body human images with aligned text descriptions and rich human-centric control conditions. Extensive quantitative and qualitative evaluations on the MiCoGen dataset show that HumanFlow consistently achieves improved structural consistency than the existing diffusion-based and flow-matching-based methods, while maintaining high visual fidelity.

Recently, since diffusion models show great potential in image generation, many pretrained diffusion models based image composition methods have been proposed for image illumination harmonization. However, they mainly face two key challenges: 1) the effective preservation of foreground appearance (i.e., content structure and texture details, etc); 2) Reasonable generation of the foreground casting shadow. To this end, we propose a novel Image Illumination Harmonization Diffusion model called I 2 HDiffuser to achieve image illumination harmonization with high-fidelity foreground appearance and reasonable cast shadows. I 2 HDiffuser mainly consists of frequency domain feature enhancement branch (FDFEB) and illumination-shadow consistency generation branch (ISCGB). Specifically, FDFEB first introduces the Wavelet Transform Module (WTM) for decomposing composite image features into low-frequency (i.e., illumination features, etc) and high-frequency (i.e., texture and content structure features, etc) components using the Haar wavelet transform. Then the Multi-Condition Guidance Mechanism (M-CGM) is proposed to interact these components as prior conditions, which are further injected into the ISCGB with a noise-to-denoise process for guiding high-fidelity content and background illumination-aware foreground regeneration. Meanwhile, a shadow mask step-wise iterative optimization strategy is introduced to the ISCGB to explicitly provide a reasonable shadow generation space for foreground objects. Extensive experiments on public image harmonization datasets DESOBAv2 and iHarmony4 and real illumination harmonization dataset IH-SG show that the I 2HDiffuser achieves the superiority.

To effectively evaluate the gross motor ability of autistic children, we proposed a method of computerised evaluation of gross motor skills (CEGM). The CEGM integrates Dynamic Time Warping (DTW) method and OpenPose technology to automatically detect key joints and return a score. Ten items were selected for evaluation based on the gross motor subtest of the Psychoeducational Profile – Third Edition (PEP-3) scale, including upper limb movement, lower limb movement, and body coordination performance. 30 autistic participants (males: 23, female: 7) with an average age of 5.00 years were recruited in this study. Then we compared the results of evaluation using CEGM and the original PEP-3 gross motor subtest in autistic children. The results showed that in the evaluations using CEGM and PEP-3, Cronbach’s α coefficients and Spearman-rank correlation coefficients were all greater than 0.80, intraclass correlation coefficient (ICC) were all greater than 0.90, indicating good agreement in evaluating the gross motor ability of autistic children. Moreover, compared to the PEP-3, the evaluation using CEGM provided precise quantitative indicators (trajectory, velocity, and angle of joint). Therefore, our findings demonstrate that CEGM can be used in the initial evaluation of the gross motor ability of autistic children.

Since being proposed in 2017, the posit number system has attracted much attention due to its advantages over the IEEE 754 standard floating-point format for better dynamic range and higher accuracy, which are crucial to many applications such as neural networks. Those advantages are yielded from a varying-length segment, regime bits, which lead to the size variations for all rest components except the sign bit. Consequently, it requires an extra decoding process to extract the numerical value of a posit number. The state-of-the-art posit decoder is designed based on a leading one/zero detector. However, we find that this conventional method holds implicit redundancy when dealing with binary numbers. In this paper, we design a novel hardware architecture, i.e., the leading difference detector, to optimize the circuit operation by eliminating the redundancy. The experimental results show that the proposed architecture can decrease the delay and power consumption by over 41% compared to the conventional designs for 8-bit, 16-bit, 32-bit, and 64-bit posit decoders.

Various applications require realistic, artifact-free, and animatable 3D avatars. However, traditional 3D morphable models (3DMMs) produce animatable 3D heads but fail to capture accurate geometries and details, while existing deep implicit functions have been shown to achieve realistic reconstructions but suffer from artifacts and struggle to yield 3D heads that are easy to animate. To reconstruct high-fidelity, artifact-less, and animatable 3D heads from single-view images, we leverage semantics to bridge the best properties of 3DMMs and deep implicit functions and propose SeIF—a semantic-constrained deep implicit function. First, SeIF derives fine-grained semantics from a standard 3DMM (e.g., FLAME) and samples a semantic code for each query point in the query space to provide a soft constraint to the deep implicit function. The reconstruction results show that this semantic constraint does not weaken the powerful representation ability of the deep implicit function while significantly suppressing artifacts. Second, SeIF predicts a more accurate semantic code for each query point and utilizes the semantic codes to uniformize the structure of reconstructed 3D head meshes with the standard 3DMM. Since our reconstructed 3D head meshes have the same structure as the 3DMM, 3DMM-based animation approaches can be easily transferred to animate our reconstructed 3D heads. As a result, SeIF can reconstruct high-fidelity, artifact-less, and animatable 3D heads from single-view images of individuals with diverse ages, genders, races, and facial expressions. Quantitative and qualitative experimental results on seven datasets show that SeIF outperforms existing state-of-the-art methods by a large margin. The code and models of SeIF are available for research purposes athttps://github.com/starVisionTeam/SeIF.

Reinforcement learning (RL) is increasingly being used to optimize resource-constrained wireless Internet of Things (IoT) devices. However, existing RL algorithms that are lightweight enough to be implemented on these devices, such as$Q$-learning, converge too slowly to effectively adapt to the experienced information source and channel dynamics, while deep RL algorithms are too complex to be implemented on these devices. By integrating basic models of the IoT system into the learning process, the so-called postdecision state (PDS)-based RL can achieve faster convergence speeds than these alternative approaches at lower complexity than deep RL; however, its complexity may still hinder the real-time and energy-efficient operations on IoT devices. In this article, we develop efficient hardware accelerators for PDS-based RL. We first develop an arithmetic hardware acceleration architecture and then propose a stochastic computing (SC)-based reconfigurable hardware architecture. By using simple bitwise computations enabled by SC, we eliminate costly multiplications involved in PDS learning, which simultaneously reduces the hardware area and power consumption. We show that the computational efficiency can be further improved by using extremely short stochastic representations without sacrificing learning performance. We demonstrate our proposed approach on a simulated wireless IoT sensor that must transmit delay-sensitive data over a fading channel while minimizing its energy consumption. Our experimental results show that our arithmetic accelerator is$5.3\times $faster than$Q$-learning and$2.6\times $faster than a baseline hardware architecture, while the proposed SC-based architecture further reduces the critical path of the arithmetic accelerator by 87.9%.