Wenyi Xu

Yian Zhao, Wanshi Xu, Yang Wu et al.

3D editing plays a crucial role in editing and reusing existing 3D assets, thereby enhancing productivity. Recently, 3DGS-based methods have gained increasing attention due to their efficient rendering and flexibility. However, achieving desired 3D editing results often requires multiple adjustments in an iterative loop, resulting in tens of minutes of training time cost for each attempt and a cumbersome trial-and-error cycle for users. This in-the-loop training paradigm results in a poor user experience. To address this issue, we introduce the concept of process-oriented modelling for 3D editing and propose the Progressive Gaussian Differential Field (ProGDF), an out-of-loop training approach that requires only a single training session to provide users with controllable editing capability and variable editing results through a user-friendly interface in real-time. ProGDF consists of two key components: Progressive Gaussian Splatting (PGS) and Gaussian Differential Field (GDF). PGS introduces the progressive constraint to extract the diverse intermediate results of the editing process and employs rendering quality regularization to improve the quality of these results. Based on these intermediate results, GDF leverages a lightweight neural network to model the editing process. Extensive results on two novel applications, namely controllable 3D editing and flexible fine-grained 3D manipulation, demonstrate the effectiveness, practicality and flexibility of the proposed ProGDF.

Qi Li, Wendong Huang, Qichen Ye et al.

The E-commerce advertising platforms typically sell commercial traffic through either second-price auction (SPA) or first-price auction (FPA). SPA was historically prevalent due to its dominant strategy incentive-compatible (DSIC) for bidders with quasi-linear utilities, especially when budgets are not a binding constraint, while FPA has gained more prominence for offering higher revenue potential to publishers and avoiding the possibility for discriminatory treatment in personalized reserve prices. Meanwhile, on the demand side, advertisers are increasingly adopting platform-wide marketing solutions akin to QuanZhanTui, shifting from spending budgets solely on commercial traffic to bidding on the entire traffic for the purpose of maximizing overall sales. For automated bidding systems, such a trend poses a critical challenge: determining optimal strategies across heterogeneous auction channels to fulfill diverse advertiser objectives, such as maximizing return (MaxReturn) or meeting target return on ad spend (TargetROAS). To overcome this challenge, this work makes two key contributions. First, we derive an efficient solution for optimal bidding under FPA channels, which takes into account the presence of organic traffic - traffic can be won for free. Second, we introduce a marginal cost alignment (MCA) strategy that provably secures bidding efficiency across heterogeneous auction mechanisms. To validate performance of our developed framework, we conduct comprehensive offline experiments on public datasets and large-scale online A/B testing, which demonstrate consistent improvements over existing methods.