Generating long and consistent videos has emerged as a significant yet
challenging problem. While most existing diffusion-based video generation
models, derived from image generation models, demonstrate promising performance
in generating short videos, their simple conditioning mechanism and sampling
strategy-originally designed for image generation-cause severe performance
degradation when adapted to long video generation. This results in prominent
temporal inconsistency and overexposure. Thus, in this work, we introduce
FlexiFilm, a new diffusion model tailored for long video generation. Our
framework incorporates a temporal conditioner to establish a more consistent
relationship between generation and multi-modal conditions, and a resampling
strategy to tackle overexposure. Empirical results demonstrate FlexiFilm
generates long and consistent videos, each over 30 seconds in length,
outperforming competitors in qualitative and quantitative analyses. Project
page: this https URL

本研究介绍了一种针对生成长视频的新扩散模型 FlexiFilm，通过引入时间条件器和重新采样策略，实现了更一致的生成效果和解决过度曝光问题。实证结果表明 FlexiFilm 在定性和定量分析中优于竞争对手，能够生成超过 30 秒的长且一致的视频。

FlexiFilm: 长视频生成与灵活条件

FlexiFilm: Long Video Generation with Flexible Conditions

Exposure correction aims to enhance images suffering from improper exposure
to achieve satisfactory visual effects. Despite recent progress, existing
methods generally mitigate either overexposure or underexposure in input
images, and they still struggle to handle images with mixed exposure, i.e., one
image incorporates both overexposed and underexposed regions. The mixed
exposure distribution is non-uniform and leads to varying representation, which
makes it challenging to address in a unified process. In this paper, we
introduce an effective Region-aware Exposure Correction Network (RECNet) that
can handle mixed exposure by adaptively learning and bridging different
regional exposure representations. Specifically, to address the challenge posed
by mixed exposure disparities, we develop a region-aware de-exposure module
that effectively translates regional features of mixed exposure scenarios into
an exposure-invariant feature space. Simultaneously, as de-exposure operation
inevitably reduces discriminative information, we introduce a mixed-scale
restoration unit that integrates exposure-invariant features and unprocessed
features to recover local information. To further achieve a uniform exposure
distribution in the global image, we propose an exposure contrastive
regularization strategy under the constraints of intra-regional exposure
consistency and inter-regional exposure continuity. Extensive experiments are
conducted on various datasets, and the experimental results demonstrate the
superiority and generalization of our proposed method. The code is released at:
this https URL

通过自适应学习和连接不同区域的曝光表示，我们介绍了一种有效的面向区域的曝光校正网络（RECNet），可以处理混合曝光的图像。通过区域感知的去曝光模块和混合尺度恢复单元，我们可以处理混合曝光不均匀分布所带来的挑战，并且通过曝光对比正则化策略实现全局图像的均匀曝光分布。在各种数据集上进行的广泛实验证明了我们提出方法的优越性和泛化性。

针对混合曝光校正的区域感知曝光一致性网络

Region-Aware Exposure Consistency Network for Mixed Exposure Correction

In traditional models for word-of-mouth recommendations and viral marketing,
the objective function has generally been based on reaching as many people as
possible. However, a number of studies have shown that the indiscriminate
spread of a product by word-of-mouth can result in overexposure, reaching
people who evaluate it negatively. This can lead to an effect in which the
over-promotion of a product can produce negative reputational effects, by
reaching a part of the audience that is not receptive to it.
How should one make use of social influence when there is a risk of
overexposure? In this paper, we develop and analyze a theoretical model for
this process; we show how it captures a number of the qualitative phenomena
associated with overexposure, and for the main formulation of our model, we
provide a polynomial-time algorithm to find the optimal marketing strategy. We
also present simulations of the model on real network topologies, quantifying
the extent to which our optimal strategies outperform natural baselines

本文提出了一种理论模型，研究了当存在过度曝光风险时如何利用社交影响力，展现了与过度曝光相关的定性现象，并提供了一个多项式时间算法来找到最佳的营销策略，同时在实际的网络拓扑上进行模拟并量化了我们的最优策略相对于自然基线的优越性。