Paper2Video: AI Turns Your Research Paper into a TED-Worthy Talk—One-Click Magic for Academic Videos

Hey, remember that NeurIPS submission crunch last year? You finally nail the paper after weeks of grinding through datasets and equations, only to face the real nightmare: crafting a 5-minute presentation video. Slide design, script polishing, voiceovers, subtitles… it sucks up an entire weekend. And don’t get me started on those cringe moments—stumbling over words or slides glitching mid-load. Enter Paper2Video, your AI “presentation clone.” Feed it your LaTeX source, a headshot, and a 10-second voice clip, and out pops a pro-level video: sleek slides, pinpoint cursor highlights, and a talking head that looks eerily like you. No hype—this is legit work from Show Lab at NUS, even accepted to a NeurIPS 2025 workshop. Stick around as we unpack the pain points, dive into hands-on setup, and geek out on tweaks that make it shine. By the end, you’ll be shipping videos faster than you can say “citations needed.”

Quick Wins: Why This Tool Makes Video Creation Actually Fun

In a nutshell, Paper2Video isn’t just a text-to-speech hack—it’s a multi-agent powerhouse called PaperTalker, laser-focused on academic video woes: digesting long papers, syncing multimodal elements (text, charts, audio), and personalizing the delivery (AI “impersonating” you with flair). The benchmark dataset packs 101 conference papers paired with videos, averaging 16 slides and 6:15 runtime, spanning ML, CV, and NLP. The payoff? Slash hours off production time while built-in metrics ensure your vid isn’t just pretty—it’s persuasive. Picture reviewers hitting play on your submission and thinking, “This idea slaps”—that’s the dream we’re chasing.

Paper2Video Overview
This teaser visualizes PaperTalker’s pipeline: from paper to slides, subtitles, cursors, and talking-head video. Feels like sci-fi? Nah, it’s just smart agents teaming up for efficiency gains.

The Hidden Drains of Academic Videos: From Manual Hell to AI Freedom

Before we crack open the terminal, let’s vent: Traditional presentation videos aren’t TikToks—they must faithfully unpack your paper’s core (motivation, methods, results) while staying audience-friendly. No crammed charts turning into visual soup, no robotic narration lulling folks to sleep. The real beast? Orchestration: Slides need subtitles, cursors must spotlight key formulas, and the presenter should feel natural (gestures optional but gold). I’ve battled off-the-shelf tools like PPTAgent for slides plus TTS—end result? Messy layouts, stiff audio, and viewers left wondering, “What’s the big idea here?”

Paper2Video nails these head-on. It spins up LaTeX Beamer slides (academic polish, lightning-fast compile), refines layouts via tree search (fixing LLMs’ blind spots on sizing), aligns cursors with WhisperX for temporal sync, and renders personalized talking heads using Hallo2. The benchmark game-changer? Ditch generic FVD scores for four tailored metrics: Meta Similarity (human-AI alignment), PresentArena (head-to-head battles), PresentQuiz (does the video teach?), and IP Memory (do viewers link you to your work?). Benchmarks show it crushes baselines by 10% on quiz accuracy, with user studies calling it “indistinguishable from human.”

Benchmark vs. Existing Tools	Input	Output	Subtitles	Slides	Cursor	Talking Head
VBench (Natural Video)	Text	Short Vid	✗	✗	✗	✗
PPTAgent	Doc + Template	Slides	✗	✓	✗	✗
PresentAgent	Doc + Template	Audio + Long Vid	✓	✓	✗	✗
Paper2Video (Ours)	Paper + Image + Audio	Audio + Long Vid	✓	✓	✓	✓

This table stacks Paper2Video against the field: It’s the first full-stack benchmark for academic videos, bridging slides to speaker personalization.

Hands-On Setup: Build Your AI Presentation Studio from Scratch

Enough theory—let’s build. Say you’ve got a LaTeX paper (like Hinton’s “Distilling the Knowledge in a Neural Network”), a square headshot, and a 10-second audio ref. Goal: One-command video gen. It’s like Lego: Modular, satisfying, and way less frustrating than manual edits.

Step 1: Environment Setup (Nail It in 10 Minutes)
Fire up your terminal and isolate environments to dodge package drama (we’ve all been bitten by numpy version hell).

cd src  # Assuming you've cloned: git clone https://github.com/showlab/Paper2Video
conda create -n p2v python=3.10
conda activate p2v
pip install -r requirements.txt
conda install -c conda-forge tectonic  # LaTeX compiler for Beamer

Next, spin up a dedicated Hallo2 env for talking heads (avoids conflicts):

git clone https://github.com/fudan-generative-vision/hallo2.git
cd hallo2
conda create -n hallo python=3.10
conda activate hallo
pip install -r requirements.txt
# Grab model weights per Hallo2's README

Cap it with API keys (GPT-4o or Gemini 1.5 Pro shine; Qwen local works too):

export GEMINI_API_KEY="your_key"
export OPENAI_API_KEY="your_key"

Run which python in the hallo env and jot the path—you’ll need it later.

Step 2: One-Click Video Magic (The Heart-Pounding Pipeline)
Cue the fireworks: pipeline.py chains it all—slides to talking head—with slide-parallel processing (6x speedup!). Minimal runnable example (right here in the action):

python pipeline.py \
    --model_name_t gpt-4o \
    --model_name_v gpt-4o \
    --model_name_talking hallo2 \
    --result_dir ./output \
    --paper_latex_root ./example_paper  # Your LaTeX project root
    --ref_img ./author_head.png  # Square portrait
    --ref_audio ./ref_audio_10s.wav  # Voice ref
    --talking_head_env /path/to/hallo  # Hallo Python path
    --gpu_list [0]  # Single GPU start; A6000 48GB recommended

Input: LaTeX source + PNG + WAV.
Output: ./output folder with slides, subs, audio, and final MP4 (~6 mins).
Expected: Clean layouts (no overfulls), cursor-nailing formulas, natural speech (Gemini scores >4/5). I ran Hinton’s paper—output felt like him on a Zoom call. Testers raved: “Pro and approachable.”

Method Pipeline
Breaking down PaperTalker: Tree search picks layouts, WhisperX syncs cursors, Hallo2 crafts talking heads.

Step 3: Benchmark Your Clone’s Performance
Don’t ship blind—vet with the benchmark. Fresh env:

cd src/evaluation
conda create -n p2v_e python=3.10
conda activate p2v_e
pip install -r requirements.txt

MetaSim (alignment check):

python MetaSim_content.py --r ./output --g ./gt_dir --s ./scores
python MetaSim_audio.py --r ./output --g ./gt_dir --s ./scores

PresentArena (showdown):

python PresentArena.py --r ./output --g ./gt_dir --s ./scores

PresentQuiz (comprehension test): Gen questions first

cd PresentQuiz
python create_paper_questions.py --paper_folder ./data
python PresentQuiz.py --r ./output --g ./gt_dir --s ./scores

IP Memory (recall sim):

cd IPMemory
python construct.py
python ip_qa.py

Score >3/5? Green light for submission. Dataset lives on Hugging Face: https://huggingface.co/datasets/ZaynZhu/Paper2Video.

Pro Tweaks: Make AI “Get” You Even Better

Leveling up? Dive into tree search visual choice—LLMs flop on fine tweaks like font scaling or chart fits, so this branches params (e.g., font=12/14/16), renders options, and lets a VLM pick the winner (no overfull + max coverage). Pseudo-code vibe: Explore param trees, VLM judges. Boom—30% layout boost, from “subway crush” to “first class.”

Customize? Flip --if_tree_search False to skip, or --stage "[1,2]" for partial runs. Local Qwen? Swap via Paper2Poster inspo in model_name_t. Pro tip with a wink: Don’t let tree search rabbit-hole your GPU overnight.

Tree Search Before/After
Optimization magic: Left’s overfull mess; right’s pristine coverage.

The Horizon: AI Isn’t Just Writing Papers—It’s Selling Them

Paper2Video hints at academia’s AI pivot: Standardized submission vids, smoother reviews, skyrocketing author visibility. It edges humans by 10% on quiz retention, with studies deeming it “near-identical.” Next frontier? Fuse with Veo3 for longer clips or gesture gen. Bottom line: From “drafting papers” to “AI pitching them,” your bandwidth frees up for bolder ideas.

FAQ: Your Burning Questions Answered
Q: No A6000 GPU? Now what?
A: Kick off with single [0]; multi-GPU via --gpu_list [0,1] speeds cursors/heads. Colab Pro works, but watch API quotas.

Q: Video feels off—debug tips?
A: Tweak ref audio (10s sweet spot) or add --ref_text for style nudges. Low scores? Pit against baselines in PresentArena.

Q: How to tap the open dataset?
A: Snag from Hugging Face, rerun eval scripts for repro. Contribute? Fork GitHub, toss in your paper-video pair.

Engineering Checklist (Copy-Paste to Your GitHub Issue)

[ ] Env: conda create -n p2v python=3.10; pip install -r requirements.txt
[ ] APIs: export GEMINI_API_KEY=your_key; export OPENAI_API_KEY=your_key
[ ] Models: git clone hallo2; Download weights
[ ] Inputs: LaTeX root + square PNG headshot + 10s WAV
[ ] Run: pipeline.py w/ GPU list; Inspect output/video.mp4
[ ] Eval: Fire MetaSim/PresentQuiz; >3/5?
[ ] Extend: Test --if_tree_search False; Diff layouts

Two Quick Challenges: 1. Gen a vid from your thesis—gauge IP Memory: Will viewers actually remember you? 2. Swap to local Gemini, time it, and share your benchmarks!