GEA: Reconstructing Expressive 3D Gaussian Avatar from Monocular Video

This paper presents GEA, a novel method for creating expressive 3D avatars with high-fidelity reconstructions of body and hands based on 3D Gaussians. The key contributions are twofold. First, we design a two-stage pose estimation method to obtain an accurate SMPL-X pose from input images, providing a correct mapping between the pixels of a training image and the SMPL-X model. It uses an attention-aware network and an optimization scheme to align the normal and silhouette between the estimated SMPL-X body and the real body in the image. Second, we propose an iterative re-initialization strategy to handle unbalanced aggregation and initialization bias faced by Gaussian representation. This strategy iteratively redistributes the avatar's Gaussian points, making it evenly distributed near the human body surface by applying meshing, resampling and re-Gaussian operations. As a result, higher-quality rendering can be achieved. Extensive experimental analyses validate the effectiveness of the proposed model, demonstrating that it achieves state-of-the-art performance in photorealistic novel view synthesis while offering fine-grained control over the human body and hand pose. Project page: https://3d-aigc.github.io/GEA/.

MS-DETR: Efficient DETR Training with Mixed Supervision

DETR accomplishes end-to-end object detection through iteratively generating multiple object candidates based on image features and promoting one candidate for each ground-truth object. The traditional training procedure using one-to-one supervision in the original DETR lacks direct supervision for the object detection candidates. We aim at improving the DETR training efficiency by explicitly supervising the candidate generation procedure through mixing one-to-one supervision and one-to-many supervision. Our approach, namely MS-DETR, is simple, and places one-to-many supervision to the object queries of the primary decoder that is used for inference. In comparison to existing DETR variants with one-to-many supervision, such as Group DETR and Hybrid DETR, our approach does not need additional decoder branches or object queries. The object queries of the primary decoder in our approach directly benefit from one-to-many supervision and thus are superior in object candidate prediction. Experimental results show that our approach outperforms related DETR variants, such as DN-DETR, Hybrid DETR, and Group DETR, and the combination with related DETR variants further improves the performance.

GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization

This paper presents GIR, a 3D Gaussian Inverse Rendering method for relightable scene factorization. Compared to existing methods leveraging discrete meshes or neural implicit fields for inverse rendering, our method utilizes 3D Gaussians to estimate the material properties, illumination, and geometry of an object from multi-view images. Our study is motivated by the evidence showing that 3D Gaussian is a more promising backbone than neural fields in terms of performance, versatility, and efficiency. In this paper, we aim to answer the question: ``How can 3D Gaussian be applied to improve the performance of inverse rendering?'' To address the complexity of estimating normals based on discrete and often in-homogeneous distributed 3D Gaussian representations, we proposed an efficient self-regularization method that facilitates the modeling of surface normals without the need for additional supervision. To reconstruct indirect illumination, we propose an approach that simulates ray tracing. Extensive experiments demonstrate our proposed GIR's superior performance over existing methods across multiple tasks on a variety of widely used datasets in inverse rendering. This substantiates its efficacy and broad applicability, highlighting its potential as an influential tool in relighting and reconstruction. Project page: https://3dgir.github.io

Layered Rendering Diffusion Model for Zero-Shot Guided Image Synthesis

This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries. We present two key innovations: Vision Guidance and the Layered Rendering Diffusion (LRDiff) framework. Vision Guidance, a spatial layout condition, acts as a clue in the perturbed distribution, greatly narrowing down the search space, to focus on the image sampling process adhering to the spatial layout condition. The LRDiff framework constructs an image-rendering process with multiple layers, each of which applies the vision guidance to instructively estimate the denoising direction for a single object. Such a layered rendering strategy effectively prevents issues like unintended conceptual blending or mismatches, while allowing for more coherent and contextually accurate image synthesis. The proposed method provides a more efficient and accurate means of synthesising images that align with specific spatial and contextual requirements. We demonstrate through our experiments that our method provides better results than existing techniques both quantitatively and qualitatively. We apply our method to three practical applications: bounding box-to-image, semantic mask-to-image and image editing.

HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception

Model pre-training is essential in human-centric perception. In this paper, we first introduce masked image modeling (MIM) as a pre-training approach for this task. Upon revisiting the MIM training strategy, we reveal that human structure priors offer significant potential. Motivated by this insight, we further incorporate an intuitive human structure prior - human parts - into pre-training. Specifically, we employ this prior to guide the mask sampling process. Image patches, corresponding to human part regions, have high priority to be masked out. This encourages the model to concentrate more on body structure information during pre-training, yielding substantial benefits across a range of human-centric perception tasks. To further capture human characteristics, we propose a structure-invariant alignment loss that enforces different masked views, guided by the human part prior, to be closely aligned for the same image. We term the entire method as HAP. HAP simply uses a plain ViT as the encoder yet establishes new state-of-the-art performance on 11 human-centric benchmarks, and on-par result on one dataset. For example, HAP achieves 78.1% mAP on MSMT17 for person re-identification, 86.54% mA on PA-100K for pedestrian attribute recognition, 78.2% AP on MS COCO for 2D pose estimation, and 56.0 PA-MPJPE on 3DPW for 3D pose and shape estimation.

Accelerating Vision Transformers Based on Heterogeneous Attention Patterns

Recently, Vision Transformers (ViTs) have attracted a lot of attention in the field of computer vision. Generally, the powerful representative capacity of ViTs mainly benefits from the self-attention mechanism, which has a high computation complexity. To accelerate ViTs, we propose an integrated compression pipeline based on observed heterogeneous attention patterns across layers. On one hand, different images share more similar attention patterns in early layers than later layers, indicating that the dynamic query-by-key self-attention matrix may be replaced with a static self-attention matrix in early layers. Then, we propose a dynamic-guided static self-attention (DGSSA) method where the matrix inherits self-attention information from the replaced dynamic self-attention to effectively improve the feature representation ability of ViTs. On the other hand, the attention maps have more low-rank patterns, which reflect token redundancy, in later layers than early layers. In a view of linear dimension reduction, we further propose a method of global aggregation pyramid (GLAD) to reduce the number of tokens in later layers of ViTs, such as Deit. Experimentally, the integrated compression pipeline of DGSSA and GLAD can accelerate up to 121% run-time throughput compared with DeiT, which surpasses all SOTA approaches.

Forward Flow for Novel View Synthesis of Dynamic Scenes

This paper proposes a neural radiance field (NeRF) approach for novel view synthesis of dynamic scenes using forward warping. Existing methods often adopt a static NeRF to represent the canonical space, and render dynamic images at other time steps by mapping the sampled 3D points back to the canonical space with the learned backward flow field. However, this backward flow field is non-smooth and discontinuous, which is difficult to be fitted by commonly used smooth motion models. To address this problem, we propose to estimate the forward flow field and directly warp the canonical radiance field to other time steps. Such forward flow field is smooth and continuous within the object region, which benefits the motion model learning. To achieve this goal, we represent the canonical radiance field with voxel grids to enable efficient forward warping, and propose a differentiable warping process, including an average splatting operation and an inpaint network, to resolve the many-to-one and one-to-many mapping issues. Thorough experiments show that our method outperforms existing methods in both novel view rendering and motion modeling, demonstrating the effectiveness of our forward flow motion modeling. Project page: https://npucvr.github.io/ForwardFlowDNeRF

VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation

In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion. We leverage an off-the-shelf text-to-image generation model, e.g., Stable Diffusion, to generate an image with high content quality from the text prompt, as a reference image to guide video generation. Then, we introduce an efficient cascaded latent diffusion module conditioned on both the reference image and the text prompt, for generating latent video representations, followed by a flow-based temporal upsampling step to improve the temporal resolution. Finally, we map latent video representations into a high-definition video through an enhanced video decoder. During training, we use the first frame of a ground-truth video as the reference image for training the cascaded latent diffusion module. The main characterises of our approach include: the reference image generated by the text-to-image model improves the visual fidelity; using it as the condition makes the diffusion model focus more on learning the video dynamics; and the video decoder is trained over unlabeled video data, thus benefiting from high-quality easily-available videos. VideoGen sets a new state-of-the-art in text-to-video generation in terms of both qualitative and quantitative evaluation. See \url{https://videogen.github.io/VideoGen/} for more samples.

Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation

In this paper, we study the problem of end-to-end multi-person pose estimation. State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e.g., regarding pose estimation as keypoint box detection and combining with human detection in ED-Pose, hierarchically predicting with pose decoder and joint (keypoint) decoder in PETR. We present a simple yet effective transformer approach, named Group Pose. We simply regard $K$-keypoint pose estimation as predicting a set of $N\times K$ keypoint positions, each from a keypoint query, as well as representing each pose with an instance query for scoring $N$ pose predictions. Motivated by the intuition that the interaction, among across-instance queries of different types, is not directly helpful, we make a simple modification to decoder self-attention. We replace single self-attention over all the $N\times(K+1)$ queries with two subsequent group self-attentions: (i) $N$ within-instance self-attention, with each over $K$ keypoint queries and one instance query, and (ii) $(K+1)$ same-type across-instance self-attention, each over $N$ queries of the same type. The resulting decoder removes the interaction among across-instance type-different queries, easing the optimization and thus improving the performance. Experimental results on MS COCO and CrowdPose show that our approach without human box supervision is superior to previous methods with complex decoders, and even is slightly better than ED-Pose that uses human box supervision. $\href{https://github.com/Michel-liu/GroupPose-Paddle}{\rm Paddle}$ and $\href{https://github.com/Michel-liu/GroupPose}{\rm PyTorch}$ code are available.

HD-Fusion: Detailed Text-to-3D Generation Leveraging Multiple Noise Estimation

In this paper, we study Text-to-3D content generation leveraging 2D diffusion priors to enhance the quality and detail of the generated 3D models. Recent progress (Magic3D) in text-to-3D has shown that employing high-resolution (e.g., 512 x 512) renderings can lead to the production of high-quality 3D models using latent diffusion priors. To enable rendering at even higher resolutions, which has the potential to further augment the quality and detail of the models, we propose a novel approach that combines multiple noise estimation processes with a pretrained 2D diffusion prior. Distinct from the Bar-Tal et al.s' study which binds multiple denoised results to generate images from texts, our approach integrates the computation of scoring distillation losses such as SDS loss and VSD loss which are essential techniques for the 3D content generation with 2D diffusion priors. We experimentally evaluated the proposed approach. The results show that the proposed approach can generate high-quality details compared to the baselines.