Title: Pulp Motion: Framing-aware multimodal camera and human motion generation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Camera generation, Human motion generation, Generative model, mulrt

Abstract: Treating human motion and camera trajectory generation separately overlooks a core principle of cinematography: the tight interplay between actor performance and camera work in the screen space. 
In this paper, we are the first to cast this task as a text-conditioned joint generation, aiming to maintain consistent on-screen framing while producing two heterogeneous, yet intrinsically linked, modalities: human motion and camera trajectories. 
We propose a simple, model-agnostic framework that enforces multimodal coherence via an auxiliary modality: the on-screen framing induced by projecting human joints onto the camera. This on-screen framing provides a natural and effective bridge between modalities, promoting consistency and leading to more precise joint distribution.
We first design a joint autoencoder that learns a shared latent space, together with a lightweight linear mapping from the human and camera latents to a framing latent. We then introduce Auxiliary Sampling, which exploits this linear map to steer generation toward a coherent framing modality. 
To support this task, we also introduce the PulpMotion dataset, a camera-motion and human-motion dataset with rich captions, and high-quality human motions.
Extensive experiments across DiT- and MAR-based architectures show the generality and effectiveness of our method in generating on-frame coherent camera-human motions, while also achieving gains on textual alignment for both modalities. Our qualitative results yield more cinematographically meaningful framings setting the new state of the art for this task.

TL;DR: Framing-aware multimodal camera and human motion generation with a auxiliary modality sampling term.