CapFrame: Text-Instructed Viewpoint Grounding in 3D Gaussian Scenes via Geometric Pseudo Labels

3D Gaussian Splatting (3DGS) enables photorealistic real-time novel view synthesis, yet placing a virtual camera to capture a de-sired frame remains largely manual. Existing language-guided approachesin 3D scenes mainly focus on object-centric grounding, determining whatto observe but rarely controlling how it should appear in a single frame,such as subject orientation or frame layout. To address this limitation,we introduce a new task, Text-Instructed Viewpoint Grounding (TIVG),which aims to identify a 6-DoF camera pose in a 3D Gaussian scenewhose rendered frame aligns with a text instruction. To solve this task,we propose CapFrame, a partially differentiable framework that con-verts language into geometric pseudo labels for camera pose optimization.CapFrame follows a Retrieve–Translate–Refine pipeline: it retrieves rele-vant views and ranks them through a Question-Evaluation process withMLLMs, translates the instruction into orientation and layout pseudo la-bels, and refines the camera pose via differentiable optimization with lay-out and orientation losses in 3DGS. Experiments on 38 real-world sceneswith 135 instructions indicate that CapFrame produces viewpoints betteraligned with texts than heuristic viewpoint search and adapted trajectorygeneration baselines, validated by VLM metrics, MLLM judges, and userstudies. Code is available at: https://github.com/jirongli/CapFrame