In recent years, visual generation has advanced rapidly and reshaped research at major computer vision conferences such as ECCV, ICCV, and CVPR. While generative models can now produce compelling images and videos, making them efficient remains a key bottleneck as compute, latency, and energy constraints increasingly matter.
Efficient visual generative models are essential for scalable, real-time applications across image and video generation, XR, gaming, design, and creative tooling. In practice, these systems must run across diverse environments, ranging from edge devices and mobile platforms to cloud services and interactive production pipelines.
The urgency has become even more pronounced with the rise of video generation and world models. Compared with image synthesis, long-horizon video generation places far greater pressure on compute, memory, and bandwidth, while world models introduce additional demands from real-time interaction, long-context reasoning, and repeated rollouts.
As generative systems move from research prototypes to product-facing deployments, efficiency is no longer just an optimization target. It becomes a core enabler that determines feasibility, responsiveness, operating cost, and energy usage in latency-sensitive and resource-constrained settings.
EVG highlights architectural innovation, data-efficient learning, resource-aware inference, and deployment-ready optimization strategies that can accelerate the adoption of generative models in real-world applications without compromising output quality and controllability.
