Visual storyline generation is the problem of selecting and sequencing a set of visual materials (i.e. images and video clips) to produce a video to elicit certain cognitive or emotional responses from viewers. In this paper, we enhance the viewing experience of generated visual storylines with the Shot Composition, Selection and Plotting (ShotCSP) approach. Designed for generating promotional videos in ecommerce settings, ShotCSP considers three key film-making principles into the visual storyline generation pipeline: a) proximity-aware scene transition, b) sound logic flow, and c) graphic discontinuity. We propose two novel metrics to enhance viewing experience: 1) Semantic Distance, which measures how related a shot is to the product being promoted; and 2) Salient Region Ratio, which estimates attention to product details in a shot. Through large-scale user evaluation involving 1,748 pairwise comparisons against five state-of-the-art approaches, ShotCSP achieves significantly improved viewing experience. It is a promising approach to enable AI generated promotional videos to benefit e-commerce businesses.

Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality. For these applications, the visual realism of fine-grained appearance details is crucial for production quality and user engagement. However, existing HPT methods often suffer from three fundamental issues: detail deficiency, content ambiguity and style inconsistency, which severely degrade the visual quality and realism of generated images. Aiming towards real-world applications, we develop a more challenging yet practical HPT setting, termed as Fine-grained Human Pose Transfer (FHPT), with a higher focus on semantic fidelity and detail replenishment. Concretely, we analyze the potential design flaws of existing methods via an illustrative example, and establish the core FHPT methodology by combing the idea of content synthesis and feature transfer together in a mutually-guided fashion. Thereafter, we substantiate the proposed methodology with a Detail Replenishing Network (DRN) and a corresponding coarse-to-fine model training scheme. Moreover, we build up a complete suite of fine-grained evaluation protocols to address the challenges of FHPT in a comprehensive manner, including semantic analysis, structural detection and perceptual quality assessment. Extensive experiments on the DeepFashion benchmark dataset have verified the power of proposed benchmark against start-of-the-art works, with 12%-14% gain on top-10 retrieval recall, 5% higher joint localization accuracy, and near 40% gain on face identity preservation. Our codes, models and evaluation tools will be released at https://github.com/Lotayou/RATE.

There is an emerging trend for sellers to use videos to promote their products on e-commerce platforms such as Taobao.com. Current video production workflow includes the production of visual storyline by human directors. We propose a system to automatically generate visual storyline based on the input set of visual materials (e.g. video clips or still images) and then produce a promotional video. In particular, we propose an algorithm called Shot Composition, Selection and Plotting (ShotCSP), which generates visual storylines leveraging film-making principles to improve viewing experience and perceived persuasiveness.

The ability to produce convincing textural details is essential for the fidelity of synthesized person images. Existing methods typically follow a “warping-based” strategy that propagates appearance features through the same pathway used for pose transfer. However, most fine-grained features would be lost during down-sampling, leading to over-smoothed clothes and missing details in the output images. In this paper we presents RATE-Net, a novel framework for synthesizing person images with sharp texture details. The proposed framework leverages an additional texture enhancing module to extract appearance information from the source image and estimate a fine-grained residual texture map, which helps to refine the coarse estimation from the pose transfer module. In addition, we design an effective alternate updating strategy to promote mutual guidance between two modules for better shape and appearance consistency. Experiments conducted on DeepFashion benchmark dataset have demonstrated the superiority of our framework compared with existing networks.

Video editing is currently a highly skill- and time-intensive process. One of the most important tasks in video editing is to compose the visual storyline. This paper outlines Visual Storyline Generator (VSG), an artificial intelligence (AI)-empowered system that automatically generates visual storylines based on a set of images and video footages provided by the user. It is designed to produce engaging and persuasive promotional videos with an easy-to-use interface. In addition, users can be involved in refining the AI-generated visual storylines. The editing results can be used as training data to further improve the AI algorithms in VSG.

Existing face restoration researches typically rely on either the image degradation prior or explicit guidance labels for training, which often lead to limited generalization ability over real-world images with heterogeneous degradation and rich background contents. In this paper, we investigate a more challenging and practical "dual-blind" version of the problem by lifting the requirements on both types of prior, termed as "Face Renovation"(FR). Specifically, we formulate FR as a semantic-guided generation problem and tackle it with a collaborative suppression and replenishment (CSR) approach. This leads to HiFaceGAN, a multi-stage framework containing several nested CSR units that progressively replenish facial details based on the hierarchical semantic guidance extracted from the front-end content-adaptive suppression modules. Extensive experiments on both synthetic and real face images have verified the superior performance of our HiFaceGAN over a wide range of challenging restoration subtasks, demonstrating its versatility, robustness and generalization ability towards real-world face processing applications. Code is available at https://github.com/Lotayou/Face-Renovation.

Video contents have become a critical tool for promoting products in E-commerce. However, the lack of automatic promotional video generation solutions makes large-scale video-based promotion campaigns infeasible. The first step of automatically producing promotional videos is to generate visual storylines, which is to select the building block footage and place them in an appropriate order. This task is related to the subjective viewing experience. It is hitherto performed by human experts and thus, hard to scale. To address this problem, we propose WundtBackpack, an algorithmic approach to generate storylines based on available visual materials, which can be video clips or images. It consists of two main parts, 1) the Learnable Wundt Curve to evaluate the perceived persuasiveness based on the stimulus intensity of a sequence of visual materials, which only requires a small volume of data to train; and 2) a clustering-based backpacking algorithm to generate persuasive sequences of visual materials while considering video length constraints. In this way, the proposed approach provides a dynamic structure to empower artificial intelligence (AI) to organize video footage in order to construct a sequence of visual stimuli with persuasive power. Extensive real-world experiments show that our approach achieves close to 10% higher perceived persuasiveness scores by human testers, and 12.5% higher expected revenue compared to the best performing state-of-the-art approach.

Short videos are becoming key for media consumers exploring the TV and Internet. The production of short videos however remains costly. In this paper, we present a domain specific video summarization application with idiom adaptation that leverages multimedia content analysis and insights from cinematic and persuasive domains. From the back-end, content curators can push raw materials and the pre-processing algorithms will automatically extract the features and encode them as editing idioms. Users can create personalized video summaries based on these idioms. We have validated the effectiveness of the demonstration on a TVC data-set with over 600 videos, enabling production of domain specific video summaries with combinations of editing idioms. This approach has been put into trial in the testbed at Alibaba Wood.

As short videos become an increasingly popular form of storytelling, there is a growing demand for video summarization to convey information concisely with a subset of video frames. Some criteria such as interestingness and diversity are used by existing efforts to pick appropriate segments of content. However, there lacks a mechanism to infuse insights from cinematography and persuasion into this process. As a result, the results of the video summarization sometimes deviate from the original. In addition, the exploration of the vast design space to create customized video summaries is costly for video producer. To address these challenges, we propose a domain specific and idiom adaptive video summarization approach. Specifically, our approach first segments the input video and extracts high-level information from each segment. Such labels are used to represent a collection of idioms and summarization metrics as submodular components which users can combine to create personalized summary styles in a variety of ways. In order to identify the importance of the idioms and metrics in different domains, we leverage max margin learning. Experimental results have validated the effectiveness of our approach. We also plan to release a dataset containing over 600 videos with expert annotations which can benefit further research in this area.