The traffic bandwidth costs, primarily driven by outbound traffic, comprise a significant amount of operating expenditure in CDNs, induced by the traffic from the end-users to edge servers (edge cost) and from the edge to the center servers (midgress cost). Traffic allocation is the main approach to minimizing the total bandwidth cost. The joint optimization of the total costs is challenging, specifically when the percentile charging mechanism as well as some other practical issues are considered, such as the dynamicity of midgress traffic and the granularity of traffic allocation. In this work, based on our novel miss ratio prediction mechanism, we propose the first online framework, namedIris, jointly optimizing the edge and midgress costs under the 95th percentile charging in commercial CDNs.Iriscan theoretically achieve a competitive ratio of$1+\frac {p_{e}}{\beta \cdot p_{c}}$, when the miss ratio of all domains is set as$\beta $. Here$p_{e}$and$p_{c}$are the unit bandwidth price of the edge and midgress cost, respectively.Irisis tolerant to prediction errors which can be deployed in practical CDN systems. Extensive experiments based on real data indicate thatIriscan dramatically reduce bandwidth costs by about 8.149% compared with the SOTA schemes, potentially saving millions of dollars per month for our large-scale commercial CDN collaborator.

Audio-driven talking face generation aims to synthesize video with lip movements synchronized to input audio. However, current generative techniques face challenges in preserving intricate regional textures (skin, teeth). To address the aforementioned challenges, we propose a novel framework called SegTalker to decouple lip movements and image textures by introducing segmentation as intermediate representation. Specifically, given the mask of image employed by a parsing network, we first leverage the speech to drive the mask and generate talking segmentation. Then we disentangle semantic regions of image into style codes using a mask-guided encoder. Ultimately, we inject the previously generated talking segmentation and style codes into a mask-guided StyleGAN to synthesize video frame. In this way, most of textures are fully preserved. Moreover, our approach can inherently achieve background separation and facilitate mask-guided facial local editing. In particular, by editing the mask and swapping the region textures from a given reference image (e.g. hair, lip, eyebrows), our approach enables facial editing seamlessly when generating talking face video. Experiments demonstrate that our proposed approach can effectively preserve texture details and generate temporally consistent video while remaining competitive in lip synchronization. Quantitative and qualitative results on the HDTF and MEAD datasets illustrate the superior performance of our method over existing methods.

There is a growing demand of automatically synthesizing co-speech gestures for virtual characters. However, it remains a challenge due to the complex relationship between input speeches and target gestures. Most existing works focus on predicting the next gesture that fits the data best, however, such methods are myopic and lack the ability to plan for future gestures. In this paper, we propose a novel reinforcement learning (RL) framework called RACER to generate sequences of gestures that maximize the overall satisfactory. RACER employs a vector quantized variational autoencoder to learn compact representations of gestures and a GPT-based policy architecture to generate coherent sequence of gestures autoregressively. In particular, we propose a contrastive pre-training approach to calculate the rewards, which integrates contextual information into action evaluation and successfully captures the complex relationships between multi-modal speech-gesture data. Experimental results show that our method significantly outperforms existing baselines in terms of both objective metrics and subjective human judgements. Demos can be found at https://github.com/RLracer/RACER.git.

The traffic bandwidth costs comprise a significant amount of operating expenditure in CDNs, induced by the traffic from the end-users to edge servers (edge cost) and from the edge to the center servers (midgress cost). Traffic allocation is the main approach to minimizing the total bandwidth cost. The joint optimization of the total costs is challenging, specifically when the percentile charging mechanism as well as some other practical issues are considered, such as the dynamicity of midgress traffic and the granularity of traffic allocation. In this work, based on our novel miss ratio prediction mechanism, we propose the first online framework, named Iris, jointly optimizing the edge and midgress costs under the 95th percentile charging in commercial CDNs. Iris can theoretically achieve a competitive ratio of$1+\frac{p_{e}}{\beta\cdot p_{c}}$, when the miss ratio of all domains is set as$\beta$. Here$p_{e}$and$p_{c}$are the unit bandwidth price of the edge and midgress cost, respectively. Iris is tolerant to prediction errors which can be deployed in practical CDN systems. Extensive experiments based on real data indicate that Iris can dramatically reduce bandwidth costs by about 8.149% compared with the SOTA schemes, potentially saving millions of dollars per month for our large-scale commercial CDN collaborator.

With the explosion of data transmitted over the Internet, Content Delivery Networks (CDNs) carry massive network traffic globally and suffer an increasingly higher bandwidth cost. A critical issue for CDN service providers is how to allocate network traffic among CDN facilities to reduce the total bandwidth cost without violating the quality of service. This work studies online traffic allocation in CDNs to minimize the bandwidth cost under the 95th percentile charging model. Specifically, we here take into account practical deployment issues in large-scale CDN systems, e.g., allocation granularity and deviation. We first theoretically prove the approximation hardness of the traffic allocation problem. We then propose a novel prediction-based algorithm named OnTPC, which effectively addresses constraints raised in practical deployment. Extensive experiments demonstrate that OnTPC outperforms state-of-the-art baselines and is expected to save over a million dollars per month for our large-scale commercial CDN collaborator. Moreover, the performance of OnTPC is consistently outstanding under various settings, and specifically robust to large allocation deviation.