Masked-based autoregressive models have demonstrated promising image generation capability in continuous space. However, their potential for video generation remains under-explored. Masked-based autoregressive models have demonstrated promising image generation capability in continuous space. However, their potential for video generation remains under-explored. In this paper, we propose \textbf{VideoMAR}, a concise and efficient decoder-only autoregressive image-to-video model with continuous tokens, composing temporal frame-by-frame and spatial masked generation. We first identify temporal causality and spatial bi-directionality as the first principle of video AR models, and propose the next-frame diffusion loss for the integration of mask and video generation. Besides, the huge cost and difficulty of long sequence autoregressive modeling is a basic but crucial issue. To this end, we propose the temporal short-to-long curriculum learning and spatial progressive resolution training, and employ progressive temperature strategy at inference time to mitigate the accumulation error. Furthermore, VideoMAR replicates several unique capacities of language models to video generation. It inherently bears high efficiency due to simultaneous temporal-wise KV cache and spatial-wise parallel generation, and presents the capacity of spatial and temporal extrapolation via 3D rotary embeddings. On the VBench-I2V benchmark, VideoMAR surpasses the previous state-of-the-art (Cosmos I2V) while requiring significantly fewer parameters ($9.3\%$), training data ($0.5\%$), and GPU resources ($0.2\%$).

Despite the recent significant progress in palmprint recognition, there are still challenges in scaling up this technology for real-world scenarios. One major challenge in developing practical, highly accurate recognition models is the shortage of comprehensive public datasets that can be used to evaluate performance at extremely low false accept rates (FAR). Furthermore, obtaining high-precision recognition models is greatly hindered by pattern variance, a notable challenge with the palmprint modality given the current technology pipeline. To address the above problems, we first collect a palmprint dataset, WebPalm, that contains the largest number of identities as well as images that have been disclosed so far. To reduce pattern variance, we propose RegPalm, a novel framework that unifies palmprint orientations (UPO) and learns pairwise spatial registration of palmprints (PPR) in an end-to-end manner. UPO harmonizes the pattern variance between left and right orientations, hence enhancing the network’s perceptual capabilities. PPR decreases both inter-class and intra-class pattern variance to improve the model’s ability to recognize hard examples. RegPalm reinforces the model by discriminating subtle palmprint features, thereby improving its performance under extremely low FAR. RegPalm not only surpasses the current state-of-the-art by 9.3 percentage points (pp) and 12.2 pp in TAR@FAR=1e-6 under the 1:1 and 1:3 open-set protocols, respectively, but also consistently achieves a 16 pp improvement in TAR@FAR=1e-9 on the WebPalm benchmark. The experimental results fully reveal the practicability and superiority of RegPalm in the real world.

The large variations of pose and illumination have been the great challenges to face recognition for many years. Because of these variations, many classical recognition methods fail to work. The key to solve this problem is to extract identity feature from face images. In recent years, people have been concentrating on synthesizing rotated faces, however, neglected the form of facial identity representation. In this paper, we propose Cross-generating Generative Adversarial Network (CG-GAN) to generate rotated faces while extracting discriminative identity. CG-GAN is allowed to learn a network to exchange poses and illuminations of two different subjects' picture. Within the network, each input image is resolved into a variation code and a identity code at the representation layer; then these codes are randomly combined for generating corresponding pictures. Not only does CG-GAN synthesis vivid face under desired pose from one picture, but also the represention layer is very suitable for face recognition task. We train and test CG-GAN on the Multi-PIE dataset and achieve state-of-the-art results.