Identity-preserving portrait generation has achieved tremendous advancements with the development of diffusion models. However, multi-ID generation remains challenging due to degraded identity fidelity and insufficient control over layout, pose, and expression. To address these challenges, we propose GroupPortrait, a novel approach for multi-ID portrait generation with three key innovations:(1) LatentID for high-fidelity identity preservation, (2) Facial Controller enabling layout guidance and fine-grained facial control, and (3) Mask-Attention Controller allocating identity embeddings to specific facial regions. First, the LatentID module improves identity preservation by adding LatentID loss during training. It maps latent representations to identity features and uses ID consistency loss for feedback training to improve identity retention. Since LatentID loss is calculated in latent space, it is more efficient in terms of time and GPU usage compared to the method that calculates ID loss in pixel space. Second, to enhance layout and facial controllability, the Facial Controller utilizes 3D Morphable Models (3DMM) to acquire facial shapes, poses, and expressions for each individual, imposing strong spatial conditions during the diffusion process. Finally, we propose a novel Mask-Attention Controller for multi-ID generation, which distributes ID embeddings into target facial regions by aligning the cross-attention map of LatentID with the given facial region masks. Extensive experiments demonstrate that GroupPortrait can generate group portraits with high fidelity, local harmony, and controllability.

This paper aims to bring fine-grained expression control while maintaining high-fidelity identity in portrait generation. This is challenging due to the mutual interference between expression and identity. On one hand, fine expression control signals inevitably introduce appearance-related semantics (e.g., facial contours, and ratio), which impact the identity of the generated portrait. On the other hand, even coarse-grained expression control can cause facial changes that compromise identity, since they all act on the face. Here, we introduce EmojiDiff, the first end-to-end solution that enables simultaneous control of extremely detailed expression (RGB-level) and high-fidelity identity in portrait generation. To address the above challenges, EmojiDiff adopts a two-stage scheme involving decoupled training and fine-tuning. For decoupled training, we innovate ID-irrelevant Data Iteration (IDI) to synthesize high-quality cross-identity expression pairs by separating and optimizing the processes of maintaining expression and altering identity. Training the model with this data, we effectively disentangle fine expression features in the expression template from other extraneous information (e.g., identity, skin). Subsequently, we present ID-enhanced Contrast Alignment (ICA) for further fine-tuning. ICA achieves rapid reconstruction and joint supervision of identity and expression information, thus aligning identity representations of images with and without expression control. Experimental results demonstrate that our method significantly outperforms its counterparts, achieving precise expression control with highly maintained identity, and generalizing well to various diffusion models. Project page: https://emojidiff.github.io/.

This paper investigates compressing a pre-trained deep object detector to a lightweight one by channel pruning, which has proved effective and flexible in promoting efficiency. However, the majority of existing works trim channels based on a monotonous criterion for general purposes, i.e., the importance to the task-specific loss. They are prone to overly prune intermediate layers and simultaneously leave large intra-layer redundancy, severely deteriorating the detection accuracy. To address the issues above, we propose a novel channel pruning approach with multi-granular importance estimation (MIEP), consisting of the Feature-level Object-sensitive Importance (FOI) and the Intra-layer Redundancy-aware Importance (IRI). The former puts large weights on channels that are critical for object representation through the guidance of object features from the pre-trained model, and mitigates over-pruning when combined with the task-specific loss. The latter groups highly correlated channels based on clustering, which are subsequently pruned with priority to decrease redundancy. Extensive experiments on the COCO and VOC benchmarks demonstrate that MIEP remarkably outperforms the state-of-the-art channel pruning approaches, achieves a better balance between accuracy and efficiency compared to lightweight object detectors, and generalizes well to various detection frameworks (e.g., Faster-RCNN and FSAF) and tasks (e.g., classification).

In this paper, a sparse representation based background model is introduced for video surveillance. Inspired by the fact that spatial and temporal information are both important for foreground detection, a spatial-temporal image patch, namely brick, is used as atomic unit for online subspace learning and sparse representation. Furthermore, Random Projection emerged from Compressive Sensing theory is applied to reduce the dimension of bricks so as to speed up the algorithm. Experimental results show the effectiveness of the proposed method.