6 research outputs found
Hierarchical Masked 3D Diffusion Model for Video Outpainting
Video outpainting aims to adequately complete missing areas at the edges of
video frames. Compared to image outpainting, it presents an additional
challenge as the model should maintain the temporal consistency of the filled
area. In this paper, we introduce a masked 3D diffusion model for video
outpainting. We use the technique of mask modeling to train the 3D diffusion
model. This allows us to use multiple guide frames to connect the results of
multiple video clip inferences, thus ensuring temporal consistency and reducing
jitter between adjacent frames. Meanwhile, we extract the global frames of the
video as prompts and guide the model to obtain information other than the
current video clip using cross-attention. We also introduce a hybrid
coarse-to-fine inference pipeline to alleviate the artifact accumulation
problem. The existing coarse-to-fine pipeline only uses the infilling strategy,
which brings degradation because the time interval of the sparse frames is too
large. Our pipeline benefits from bidirectional learning of the mask modeling
and thus can employ a hybrid strategy of infilling and interpolation when
generating sparse frames. Experiments show that our method achieves
state-of-the-art results in video outpainting tasks. More results are provided
at our https://fanfanda.github.io/M3DDM/.Comment: ACM MM 2023 accepte
AIGCBench: Comprehensive evaluation of image-to-video content generated by AI
The burgeoning field of Artificial Intelligence Generated Content (AIGC) is witnessing rapid advancements, particularly in video generation. This paper introduces AIGCBench, a pioneering comprehensive and scalable benchmark designed to evaluate a variety of video generation tasks, with a primary focus on Image-to-Video (I2V) generation. AIGCBench tackles the limitations of existing benchmarks, which suffer from a lack of diverse datasets, by including a varied and open-domain image–text dataset that evaluates different state-of-the-art algorithms under equivalent conditions. We employ a novel text combiner and GPT-4 to create rich text prompts, which are then used to generate images via advanced Text-to-Image models. To establish a unified evaluation framework for video generation tasks, our benchmark includes 11 metrics spanning four dimensions to assess algorithm performance. These dimensions are control-video alignment, motion effects, temporal consistency, and video quality. These metrics are both reference video-based and video-free, ensuring a comprehensive evaluation strategy. The evaluation standard proposed correlates well with human judgment, providing insights into the strengths and weaknesses of current I2V algorithms. The findings from our extensive experiments aim to stimulate further research and development in the I2V field. AIGCBench represents a significant step toward creating standardized benchmarks for the broader AIGC landscape, proposing an adaptable and equitable framework for future assessments of video generation tasks. We have open-sourced the dataset and evaluation code on the project website: https://www.benchcouncil.org/AIGCBench