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X2SAM is introduced as a unified segmentation MLLM that extends any-segmentation capabilities from images to videos by coupling an LLM with a Mask Memory module for temporally consistent video mask generation. This architecture supports diverse segmentation tasks, including open-vocabulary, referring, and interactive segmentation, across both image and video inputs. The authors also introduce the Video Visual Grounded (V-VGD) segmentation benchmark to evaluate segmentation of object tracks in videos from visual prompts, demonstrating X2SAM's strong video segmentation performance and competitive image segmentation results through joint training.
Finally, a single model that handles any segmentation task in both images and videos, understanding both text and visual prompts.
Multimodal Large Language Models (MLLMs) have demonstrated strong image-level visual understanding and reasoning, yet their pixel-level perception across both images and videos remains limited. Foundation segmentation models such as the SAM series produce high-quality masks, but they rely on low-level visual prompts and cannot natively interpret complex conversational instructions. Existing segmentation MLLMs narrow this gap, but are usually specialized for either images or videos and rarely support both textual and visual prompts in one interface. We introduce X2SAM, a unified segmentation MLLM that extends any-segmentation capabilities from images to videos. Given conversational instructions and visual prompts, X2SAM couples an LLM with a Mask Memory module that stores guided vision features for temporally consistent video mask generation. The same formulation supports generic, open-vocabulary, referring, reasoning, grounded conversation generation, interactive, and visual grounded segmentation across image and video inputs. We further introduce the Video Visual Grounded (V-VGD) segmentation benchmark, which evaluates whether a model can segment object tracks in videos from interactive visual prompts. With a unified joint training strategy over heterogeneous image and video datasets, X2SAM delivers strong video segmentation performance, remains competitive on image segmentation benchmarks, and preserves general image and video chat ability.
