#video-generation

17 articles

01 ·Time Series·★ MEMBER·PAPER·7 min read Paper Explained: Video Generation Models: A Survey of Post-Training and Alignment — Four Toolboxes for Making Video Models Do What You Ask A from-scratch reading of the survey that organizes how pretrained video generators are steered toward instruction-following, temporal coherence, physical plausibility, and safety without retraining—via supervised fine-tuning, self-training and distillation, preference and reward optimization, and inference-time methods. 02 ·★ MEMBER·PAPER·7 min read Paper Explained: WorldCrafter — A Video World Model That Remembers What You Saw, via Camera-Queryable Memory Video world models tend to repaint a room differently when you walk back into it. WorldCrafter tackles this with an implicit 3D-aware memory that is read out from the viewpoint you are about to look from. We explain it from scratch with equations, interactive figures, and code. 03 ·Inference & Serving·★ MEMBER·PAPER·8 min read Paper Explained: RewardVerse — A Video Reward Model That Writes the Rubric Before It Scores (RGPO) Video reward models tend to squeeze their scores into a narrow band, and those scores move when the wording changes. The paper calls this scalar drift. RewardVerse first generates an evaluation rubric, then reads a continuous score for each item from token probabilities. Its two-stage RGPO training uses only 30 preference pairs per dimension. 04 ·★ MEMBER·PAPER·8 min read Paper Explained: Can MiniMax-H3 Reason About the Physical World? Testing an Omni-Modal Generator on What the Prompt Leaves Out An evaluation paper that gives MiniMax-H3 — a generator that takes text, images, audio and video together — 517 tasks where the answer is never in the prompt and can only be worked out by combining the inputs. Overall success is 41.97%, and telling events apart by sound manages just 27.40%. 05 ·Training & Alignment·★ MEMBER·PAPER·8 min read Paper walkthrough: WanPE — turning a video prompt into a director's shooting plan WanPE rewrites a one-line request into a shot-by-shot plan covering camera, lighting and sound. We explain, from zero, why it builds training pairs backwards from real videos and how SC-GRPO penalizes dropped requirements. 06 ·Large Language Models·★ MEMBER·PAPER·8 min read Paper Explained: Training Object Permanence in World Models — Teaching Video Models That Hidden Things Still Exist Video generators make hidden objects vanish and let balls pass through walls. We walk through WROP, a 150-task Blender benchmark for object permanence and solidity, the 16B PWM-WROP model fine-tuned on it, and how to read the human-preference evaluation behind the results. 07 ·★ MEMBER·PAPER·12 min read Paper Deep Dive: Mask Forcing — Fixing Autoregressive Video Diffusion Distillation with a Dual-Noise Mask Distilling a bidirectional video diffusion teacher into a real-time autoregressive student tends to produce over-saturated, over-smoothed video. Mask Forcing eases this mode collapse by randomly swapping some tokens in the student's own rollout for slightly cleaner ones — no real data, no extra post-training stage. 08 ·★ MEMBER·PAPER·14 min read SolarWM: Training on 5 Seconds, Walking for an Hour — and Opening the Whole Stack A data engine that folds 10 datasets into 1.43M clips under one contract, plus a three-stage recipe that adapts four different video backbones without erasing them. The causal models roll out for minutes to hours after training on 5-second sequences alone. 09 ·Time Series·★ MEMBER·PAPER·12 min read H3-World: Turning Language Understanding into World Control — Paper Explained H3-World turns a 33B video generator into a controllable world model without bolting on an action module: express actions as sentences, bind each sentence to a video-latent interval, and use attention routing to stop the crosstalk. Only 0.199% of parameters are trained. 10 ·Agents·★ MEMBER·PAPER·14 min read Paper Walkthrough: Code World Model — Putting a Coding Agent in Charge of the World A ground-up reading of Code World Model, which hands the *evolution* of a world to executable code and its *appearance* to a video model — including the proxy interface that joins them, and the limits the authors admit to. 11 ·Training & Alignment·★ MEMBER·PAPER·10 min read PAWBench Explained — Can Video Generators Get the Odds Right, Not Just the Physics? If a video generator is a world model, it owes you more than one plausible rollout — it owes you the right distribution over futures. PAWBench measures that probabilistic alignment across 50 scenarios and 11 systems, and finds that no model gets all the requirements at once. 12 ·FREE·PAPER·9 min read Paper Explained: VGI-Bench — Can Video Models Get the Middle of the Story Right? A ground-up walkthrough of VGI-Bench, a benchmark that treats video generation models as machines that reason in pictures. Even the strongest model, Seedance 2.0, scores only 51.0 — and self-correction during denoising almost never happens. 13 ·FREE·PAPER·15 min read Paper Explained: VBVR-Pro — Making "Thinking in Pictures" Trainable, Scorable, and Comparable Native visual reasoning treats image and video generation as the medium of thought. VBVR-Pro supplies 300 procedurally generated tasks and deterministic scorers that replace the VLM judge, then pits image, video, and interleaved generation against each other on identical ground. Written for readers starting from zero. 14 ·FREE·PAPER·15 min read Paper Walkthrough: EchoWM — A World You Can Walk Into, and Hear, Driven by Camera Intent Hand it a reference view and a stream of 'where I want to go', and it keeps generating 720p video together with environmental sound, music and speech. A walkthrough of EchoWM from the paper itself: one camera-intent interface for both first and third person, a single translation scale shared across the whole dataset, and the sink-plus-FIFO cache that makes long rollouts possible. 15 ·FREE·PAPER·11 min read Paper Walkthrough: SemComp-Bench — Asking Video Generators 'So Did It Actually Get Done?' SemComp-Bench moves video-generation evaluation from 'does it look good' to 'did it reach the outcome I asked for'. A walkthrough of the task definition, the four-stage data pipeline, the AND-vs-average scoring, and the measured numbers — all from the paper itself. 16 ·Large Language Models·★ MEMBER·PAPER·8 min read Paper Walkthrough: Can Anything Catch a Fake Crisis Video? — What RA-Bench Found Sixteen thousand AI videos, each continuing from the real first frame of a genuine disaster or war clip, put against seven classical detectors, ten zero-shot multimodal models and two purpose-built fine-tunes. None of them generalized. One model turned out to be reading timestamps rather than pixels, and a lap through a social feed drops fake recall to 1.4%. 17 ·CNNs & Image Recognition·★ MEMBER·PAPER·10 min read Paper Walkthrough: PhiZero — A World Model That Reasons in a Language of Physics Before It Renders A close read of PhiZero, which reasons about how the world will change in a discrete 'physical language' learned self-supervised from video, and only then renders the footage — instead of predicting pixels directly. The two-stage tokenizer and reasoner, the top spot on Physics-IQ Verified, zero-shot motion transfer, and the limitations the paper itself admits.